patrick-llgc/Learning-Deep-Learning

Paper reading notes on Deep Learning and Machine Learning

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updated Jun 4, 2026

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README

Paper notes

This repository contains my paper reading notes on deep learning and machine learning. It is inspired by Denny Britz and Daniel Takeshi. A minimalistic webpage generated with Github io can be found here.

About me

My name is Patrick Langechuan Liu. After about a decade of training and research in physics, I found my passion in deep learning and autonomous driving.

I am currently Director of AI at Nvidia, leading the ML modeling effort for Nvidia's end-to-end autonomous driving project, Alpamayo.

What to read

If you are new to deep learning in computer vision and don't know where to start, I suggest you spend your first month or so dive deep into this list of papers. I did so (see my notes) and it served me well.

Here is a list of trustworthy sources of papers in case I ran out of papers to read.

My review posts by topics

I regularly update my blog column the Thinking Car.

Notes of AI Podcasts

Interview of Jiali Weng at OpenAI by WhynotTV [Notes] [Interview, Jiali Weng/翁家笠, OpenAI, WhynotTV]

Scratchpad by Topics

This session contains quick notes (like git-gist) to my future self.

2026-04 (1)

VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference [Notes] [Async infer, Song Han, Zhijian Liu]
RTC: Real-Time Execution of Action Chunking Flow Policies [Async infer, Physical Intelligence]
Running VLAs at Real-time Speed [Async infer, Dexmal]
Leave No Observation Behind: Real-time Correction for VLA Action Chunks [Async infer]
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics [Async infer]
FlashDrive: Flash Vision-Language-Action Inference For Autonomous Driving [Notes] ICLR 2026
Fast-WAM: Do World Action Models Need Test-time Future Imagination? [Hang Zhao]

2026-02 (1)

2026-01 (10)

Stepwise Internalization: From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step [Notes] [Yejin Choi, latent CoT]
Coconut: Training Large Language Models to Reason in a Continuous Latent Space [Notes] COLM 2025 [Yuandong Tian, latent CoT]
DLCM: Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space [Notes] [Xingwei Qu, ByteDance, latent CoT --> concept models with interpretability]
A Survey on Latent Reasoning [Notes] [Xingwei Qu, ByteDance]
Large Language Diffusion Models
Dream 7B: Diffusion Large Language Models
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference [Bytedance Seed]
MMaDA: Multimodal Large Diffusion Language Models [Bytedance Seed]
DFlash: Block Diffusion for Flash Speculative Decoding
Fast-dLLM: Training-free Acceleration of Diffusion LLM by Enabling KV Cache and Parallel Decoding [Notes] [Song Han, Enze Xie]
Fast-dLLM v2: Efficient Block-Diffusion LLM [Notes] [Song Han, Enze Xie]
Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed [Notes] [Pavlo, Song Han, Nvidia]
TiDAR: Think in Diffusion, Talk in Autoregression [Notes] [Nvidia, Pavlo]
Dream 7B: Diffusion Large Language Models
Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models ICLR 2025 Oral
SD3: Scaling Rectified Flow Transformers for High-Resolution Image Synthesis [Patrick Esser, Stable Diffusion]
Wan: Open and Advanced Large-Scale Video Generative Models [通义万象]
RHO-1: Not All Tokens Are What You Need [Notes] NeurIPS 2024 Oral
WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios [Notes] [Waymo]
LVP: Large Video Planner Enables Generalizable Robot Control [Notes]
Robust Finetuning of Vision-Language-Action Robot Policies via Parameter Merging
Epona: Autoregressive Diffusion World Model for Autonomous Driving ICCV 2025 [Horizonp]
HaMeR: Reconstructing Hands in 3D with Transformers [hand mesh recovery, Berkeley, mono recon of MANO parameterization]
MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videos CVPR 2025 Best Paper Shortlist [SaM: structure and Motion]

2025-12 (0)

DDPM: Denoising Diffusion Probabilistic Models [Berkeley, Jonathan Ho, Pieter Abbeel]
DDIM: Denoising Diffusion Implicit Models ICLR 2021 [Stanford, Stefano Ermon]
score SDE: Score-Based Generative Modeling through Stochastic Differential Equations ICLR 2021 Oral [Stanford, Yang Song, Stefano Ermon]
Improved DDPM: Improved Denoising Diffusion Probabilistic Models [OpenAI, Prafulla Dhariwal]
Classifier Guidance: Diffusion Models Beat GANs on Image Synthesis [OpenAI, Prafulla Dhariwal]
LDM: Latent Diffusion Model: High-Resolution Image Synthesis with Latent Diffusion Models CVPR 2022 [Stable Diffusion v1, Patrick Esser]
CFG: Classifier-Free Diffusion Guidance NeurIPS 2021 [Jonathan Ho, Google Brain]
Rectified flow: Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow [Xingchao Liu, UT Austin, even earlier than flow matching paper]
Flow Matching for Generative Modeling [FAIR]
DiT: Scalable Diffusion Models with Transformers [Saining Xie]
OT-CFM: Improving and generalizing flow-based generative models with minibatch optimal transport TMLR 2023 [Alex Tong]
Mean Flows for One-step Generative Modeling [Kaiming He]
iMF: Improved Mean Flows: On the Challenges of Fastforward Generative Models [Kaiming He]

2025-09 (2)

Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning [Notes] [Nvidia]
CoVLA: Comprehensive Vision-Language-Action Dataset for Autonomous Driving [Notes] WACV 2025 [~80 h real-world driving videos with paired language and trajectory annotations; largest VLA-for-driving dataset]
SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment CVPR 2025 [1st Place @ CARLA Challenge 2024; SOTA on CARLA LB 2.0 and Bench2Drive; cited by AutoVLA]
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning arXiv 2025-06 [Trajectory tokenization; dual-thinking (fast vs slow CoT) modes; GRPO fine-tuning; evaluated on nuPlan, nuScenes, Waymo, CARLA; cites SimLingo]
DriveAgent-R1: Advancing VLM-based Autonomous Driving with Hybrid Thinking and Active Perception arXiv 2025-07 [Hybrid Thinking (text vs tool based CoT) + Active Perception; 3-stage RL training; built on DriveVLM lineage]
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving [Kangan Qian, Sicong Jiang, Xiaomi] EMNLP25
VERDI: VLM-Embedded Reasoning for Autonomous Driving arXiv 2025-05 [Distills VLM reasoning into a modular AD stack; aligns perception, prediction, planning; improves nuScenes performance with no VLM inference cost]
Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Post-Training Enable Robust End-to-End Autonomous Driving arXiv 2025-06 [3B-param VLM trained on 83 h CoVLA + 11 h Waymo long-tail; RL fine-tuned (GRPO); 1st Place in Waymo Vision-Based E2E Driving Challenge (RFS=7.99)]
ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-Loop Autonomous Driving arXiv 2025-05 [Chain-of-Thought planning; significant closed-loop improvements on Bench2Drive]
DiffVLA: Vision-Language-Guided Diffusion Planning for Autonomous Driving arXiv 2025-05 [VLM-guided diffusion trajectory planning; top performance in Autonomous Grand Challenge 2025]
VLAD: A VLM-Augmented Autonomous Driving Framework ITSC 2025 [VLM generates high-level commands for E2E controller; enhances interpretability and planning safety]
DriveAction (benchmark): DriveAction: A Benchmark for Exploring Human-like Driving Decisions in VLA Models arXiv 2025-06 [Action-rooted evaluation with QA pairs across driving scenarios; cited by VLA4AD survey; gaining traction]
DINOv3 [High res Dino]
LiveVLM: Efficient Online Video Understanding via Streaming-Oriented KV Cache and Retrieval
DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation [dextrous hand data collection, Shuran Song, Jim Fan]
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation [Best Paper Award @ RSS 2025]
HAD dataset: Grounding Human-to-Vehicle Advice for Self-driving Vehicles CVPR 2019 [John Canny, Honda reesarch institute, 2019, VLA OG]
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning NeurIPS 2025 [Horizon]
PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding [FAIR]
Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models CVPR 2025 best paper candidates [Nvidia, Sanja Fidler]
π∗0.6: a VLA That Learns From Experience [VLA + RL]
π𝚁𝙻: Online RL Fine-tuning for Flow-based Vision-Language-Action Models [VLA + RL]
VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning [VLA + RL]
GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation [VLA + RL]
GR-3 Technical Report [VLA, Bytedance]
Mixture of Horizons in Action Chunking

2025-06 (1)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning [LeCun]
V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video ICLR 2025
I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture ICCV 2023
PlaNet: Learning Latent Dynamics for Planning from Pixels
DreamerV1: Dream to Control: Learning Behaviors by Latent Imagination
DreamerV2: Mastering Atari with Discrete World Models ICLR 2021
DreamerV3: Mastering Diverse Domains through World Models Nature 2025
DayDreamer: World Models for Physical Robot Learning CoRL 2022
Dynalang: Learning to Model the World with Language ICML 2024
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving [Notes] [Marco Pavone, Nvidia]
SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation [Notes] ICRA 2025 [Horizon]
HE-Drive: Human-Like End-to-End Driving with Vision Language Models IROS 2025 [Horizon]
GPT-Driver: Learning to Drive with GPT [NeurIPS 2023, Hang Zhao]
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving [Notes] ICRA 2024 [Wayve]
PARA-Drive: Parallelized Architecture for Real-time Autonomous Driving CVPR 2024 [Marco Pavone, NVidia]
PDM-Closed: Parting with Misconceptions about Learning-based Vehicle Motion Planning [Notes] CoRL 2023
Ego-MLP: Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving? CVPR 2024
AD-MLP: Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes [Baidu]
GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving [Wayve]
Cameras as Relative Positional Encoding

2025-04

2024-12 (0)

2024-11 (1)

On the Opportunities and Risks of Foundation Models [Notes]
π0: A Vision-Language-Action Flow Model for General Robot Control [Physical Intelligence, VLA]
EMMA: End-to-End Multimodal Model for Autonomous Driving [Waymo, VLA]
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data CVPR 2024
Depth Anything V2 NeurIPS 2024
CarLLaVA: Vision language models for camera-only closed-loop driving
LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias [Scene tokenization]
NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking NeurIPS 2024
Driving Everywhere with Large Language Model Policy Adaptation CVPR 2024 [Marco Pavone]
Consistency Models [difusion speedup, OpenAI, Yang Song]
VILA: On Pre-training for Visual Language Models CVPR 2024 [Song Han, Yao Lu]

2024-06 (8)

LINGO-1: Exploring Natural Language for Autonomous Driving [Notes] [Wayve, open-loop world model]
LINGO-2: Driving with Natural Language [Notes] [Wayve, closed-loop world model]
OpenVLA: An Open-Source Vision-Language-Action Model [open source RT-2]
Parting with Misconceptions about Learning-based Vehicle Motion Planning CoRL 2023 [Simple non-learning based baseline]
QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving [Waabi]
MPDM: Multipolicy decision-making in dynamic, uncertain environments for autonomous driving [Notes] ICRA 2015 [Behavior planning, UMich, May Autonomy]
MPDM2: Multipolicy Decision-Making for Autonomous Driving via Changepoint-based Behavior Prediction [Notes] RSS 2015 [Behavior planning]
MPDM3: Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment RSS 2017 [Behavior planning]
EUDM: Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching [Notes] ICRA 2020 [Wenchao Ding, Shaojie Shen, Behavior planning]
TPP: Tree-structured Policy Planning with Learned Behavior Models ICRA 2023 [Marco Pavone, Nvidia, Behavior planning]
MARC: Multipolicy and Risk-aware Contingency Planning for Autonomous Driving [Notes] RAL 2023 [Shaojie Shen, Behavior planning]
EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments TRO 2021 [Wenchao Ding, encyclopedia of pnc]
trajdata: A Unified Interface to Multiple Human Trajectory Datasets NeurIPS 2023 [Marco Pavone, Nvidia]
Optimal Vehicle Trajectory Planning for Static Obstacle Avoidance using Nonlinear Optimization [Xpeng]
Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles [Notes] IROS 2019 Oral [Uber ATG, behavioral planning, motion planning]
Enhancing End-to-End Autonomous Driving with Latent World Model
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [Jiwen Lu]
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision ICRA 2024
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision [Sanja, Marco, NV]
FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation
Trajeglish: Traffic Modeling as Next-Token Prediction ICLR 2024
Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks ITSC 2021
Learning-Based Approach for Online Lane Change Intention Prediction IV 2013 [SVM, LC intention prediction]
Traffic Flow-Based Crowdsourced Mapping in Complex Urban Scenario RAL 2023 [Wenchao Ding, Huawei, crowdsourced map]
FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow ICRA 2023
Hybrid A-star: Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments IJRR 2010 [Dolgov, Thrun, Searching]
Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Frame ICRA 2010 [Werling, Thrun, Sampling] [MUST READ for planning folks]
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method TITS 2022
Baidu Apollo EM Motion Planner [Notes][Optimization]
基于改进混合A*的智能汽车时空联合规划方法 汽车工程: 规划&决策2023年 [Joint optimization, search]
Enable Faster and Smoother Spatio-temporal Trajectory Planning for Autonomous Vehicles in Constrained Dynamic Environment JAE 2020 [Joint optimization, search]
Focused Trajectory Planning for Autonomous On-Road Driving IV 2013 [Joint optimization, Iteration]
SSC: Safe Trajectory Generation for Complex Urban Environments Using Spatio-Temporal Semantic Corridor RAL 2019 [Joint optimization, SSC, Wenchao Ding, Motion planning]
AlphaGo: Mastering the game of Go with deep neural networks and tree search [Notes] Nature 2016 [DeepMind, MTCS]
AlphaZero: A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play Science 2017 [DeepMind]
MuZero: Mastering Atari, Go, chess and shogi by planning with a learned model Nature 2020 [DeepMind]
Grandmaster-Level Chess Without Search [DeepMind]
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving [MobileEye, desire and traj optimization]
Comprehensive Reactive Safety: No Need For A Trajectory If You Have A Strategy IROS 2022 [Da Fang, Qcraft]
BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning AAAI 2024
LLM-MCTS: Large Language Models as Commonsense Knowledge for Large-Scale Task Planning NeurIPS 2023
Hivt: Hierarchical vector transformer for multi-agent motion prediction CVPR 2022 [Zikang Zhou, agent-centric, motion prediction]
QCNet: Query-Centric Trajectory Prediction [Notes] CVPR 2023 [Zikang Zhou, scene-centric, motion prediction]

2024-03 (11)

Genie: Generative Interactive Environments [Notes] [DeepMind, World Model]
DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving [Notes] [Jiwen Lu, World Model]
WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens [Notes] [Jiwen Lu, World Model]
VideoPoet: A Large Language Model for Zero-Shot Video Generation [Like sora, but LLM, NOT world model]
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models [Notes] CVPR 2023 [Sanja, Nvidia, VideoLDM, Video prediction]
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos NeurIPS 2022 [Notes] [OpenAI]
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge NeurIPS 2022 [NVidia, Outstanding paper award]
Humanoid Locomotion as Next Token Prediction [Notes] [Berkeley, EAI]
RPT: Robot Learning with Sensorimotor Pre-training [Notes] CoRL 2023 Oral [Berkeley, EAI]
MVP: Real-World Robot Learning with Masked Visual Pre-training [Notes] CoRL 2022 [Berkeley, EAI]
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning [Notes] CoRL 2021 [Eric Jang, 1X]
GenAD: Generalized Predictive Model for Autonomous Driving [Notes] CVPR 2024
HG-DAgger: Interactive Imitation Learning with Human Experts [DAgger]
DriveGAN: Towards a Controllable High-Quality Neural Simulation [Notes] CVPR 2021 oral [Nvidia, Sanja]
VideoGPT: Video Generation using VQ-VAE and Transformers [Notes] [Pieter Abbeel]
LLM, Vision Tokenizer and Vision Intelligence, by Lu Jiang [Notes] [Interview Lu Jiang]
AV2.0: Reimagining an autonomous vehicle [Notes] [Wayve, Alex Kendall]
Simulation for E2E AD [Wayve, Tech Sharing, E2E]
E2E lateral planning [Comma.ai, E2E planning]
Learning and Leveraging World Models in Visual Representation Learning [LeCun, JEPA series]
LVM: Sequential Modeling Enables Scalable Learning for Large Vision Models [Large Vision Models, Jitendra Malik]
LWM: World Model on Million-Length Video And Language With RingAttention [Pieter Abbeel]
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving [Jiwen Lu, World Model]
GenAD: Generative End-to-End Autonomous Driving
Transfuser: Multi-Modal Fusion Transformer for End-to-End Autonomous Driving CVPR 2021 [E2E planning, Geiger]
Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving [Wayve, LLM + AD]
LingoQA: Video Question Answering for Autonomous Driving [Wayve, LLM + AD]
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving CVPR 2024 [Megvii]
PlanT: Explainable Planning Transformers via Object-Level Representations CoRL 2022
Scene as Occupancy ICCV 2023
The Shift from Models to Compound AI Systems
Roach: End-to-End Urban Driving by Imitating a Reinforcement Learning Coach ICCV 2021
Learning from All Vehicles CVPR 2022
LBC: Learning by Cheating CoRL 2019
Learning to drive from a world on rails ICCV 2021 oral [Philipp Krähenbühl]
Learning from All Vehicles CVPR 2022 [Philipp Krähenbühl]
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning [Horizon]
VQ-VAE: Neural Discrete Representation Learning NeurIPS 2017 [Image Tokenizer]
VQ-GAN: Taming Transformers for High-Resolution Image Synthesis CVPR 2021 [Image Tokenizer]
ViT-VQGAN: Vector-quantized Image Modeling with Improved VQGAN ICLR 2022 [Image Tokenizer]
MaskGIT: Masked Generative Image Transformer CVPR 2022 [LLM, non-autoregressive]
MAGVIT: Masked Generative Video Transformer CVPR 2023 highlight [Video Tokenizer]
MAGVIT-v2: Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation ICLR 2024 [Video Tokenizer]
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models [Reverse Engineering of Sora]
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts ICML 2022 [MoE, LLM]
Lifelong Language Pretraining with Distribution-Specialized Experts ICML 2023 [MoE, LLM]
DriveLM: Drive on Language [Hongyang Li]
MotionLM: Multi-Agent Motion Forecasting as Language Modeling ICCV 2023 [Waymo, LLM + AD]
CubeLLM: align 2D/3D with language
EmerNeRF: ICLR 2024
A Language Agent for Autonomous Driving
[Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal]
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving CVPR 2024 [Zheng Zhu]
Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond [Zheng Zhu]

2024-02 (7)

End-to-end Autonomous Driving: Challenges and Frontiers [Notes] [Hongyang Li, Shanghai AI labs]
DriveVLM: The convergence of Autonomous Driving and Large Vision-Language Models [Notes] [Hang Zhao]
DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model [Notes] [HKU]
GAIA-1: A Generative World Model for Autonomous Driving [Notes] [Wayve, vision foundation model]
ADriver-I: A General World Model for Autonomous Driving [Notes] [Megvii, Xiangyu]
Drive-WM: Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving [Notes]
X [Notes] [E2E planning]

2023-12 (4)

ChatGPT for Robotics: Design Principles and Model Abilities [Notes] [Microsoft, LLM for robotics]
RoboVQA: Multimodal Long-Horizon Reasoning for Robotics [Notes] [Google DeepMind, LLM for robotics]
ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application [Microsoft Robotics]
GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration [Notes] [LLM for robotics, Microsoft Robotics]
LLM-Brain: LLM as A Robotic Brain: Unifying Egocentric Memory and Control [Notes]
Voyager: An Open-Ended Embodied Agent with Large Language Models [Notes] [Reasoning Critique, Linxi Jim Fan]

2023-09 (3)

RetNet: Retentive Network: A Successor to Transformer for Large Language Models [Notes] [MSRA]
Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention [Notes] ICML 2020 [Linear attention]
AFT: An Attention Free Transformer [Notes] [Apple]

2023-08 (3)

RT-1: Robotics Transformer for Real-World Control at Scale [Notes] [DeepMind]
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control [Notes] [DeepMind, end-to-end visuomotor]
RWKV: Reinventing RNNs for the Transformer Era [Notes]

2023-07 (6)

MILE: Model-Based Imitation Learning for Urban Driving [Notes] NeurIPS 2022 [Alex Kendall]
PaLM-E: An embodied multimodal language model [Notes] [Google Robotics]
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models [Notes] [Feifei Li]
CaP: Code as Policies: Language Model Programs for Embodied Control [Notes] [Project]
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models ICRA 2023
TidyBot: Personalized Robot Assistance with Large Language Models [Notes] [Project]
SayCan: Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [Notes] [Project]

2023-06 (5)

End-to-end review by Shanghai AI Labs
Pix2seq v2: A Unified Sequence Interface for Vision Tasks [Notes] NeurIPS 2022 [Geoffrey Hinton]
🦩 Flamingo: a Visual Language Model for Few-Shot Learning [Notes] NeurIPS 2022 [DeepMind]
😼 Gato: A Generalist Agent [Notes] TMLR 2022 [DeepMind]
BC-SAC: Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios [Notes] NeurIPS 2022 [Waymo]
MGAIL-AD: Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving [Notes] IROS 2022 [Waymo]

2023-05 (7)

SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving [Notes] [Occupancy Network, Wei Yi, Jiwen Lu]
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving [Notes] [Occupancy Network, Zhao Hang]
Occupancy Networks: Learning 3D Reconstruction in Function Space CVPR 2019 [Notes] [Andreas Geiger]
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction [Occupancy Network, PhiGent]
Pix2seq: A Language Modeling Framework for Object Detection [Notes] ICLR 2022 [Geoffrey Hinton]
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks [Notes] [Jifeng Dai]
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face [Notes]

2023-04 (1)

UniAD: Planning-oriented Autonomous Driving [Notes] CVPR 2023 best paper [BEV, e2e, Hongyang Li]

2023-03 (5)

GPT-4 Technical Report [Notes] [OpenAI, GPT]
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception [Notes] [Occupancy Network, Jiwen Lu]
VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completion [Note] CVPR 2023 highlight [Occupancy Network, Nvidia]
MonoScene: Monocular 3D Semantic Scene Completion CVPR 2022 [Notes] [Occupancy Network, single cam]
CoReNet: Coherent 3D scene reconstruction from a single RGB image [Notes] ECCV 2020 oral

2023-02 (4)

Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning [Notes] [Epoch.ai industry report]
Codex: Evaluating Large Language Models Trained on Code [Notes] [GPT, OpenAI]
InstructGPT: Training language models to follow instructions with human feedback [Notes] [GPT, OpenAI]
TPVFormer: Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction [Notes] CVPR 2023 [Occupancy Network, Jiwen Lu]

2023-01 (2)

2022-11 (1)

M2I: From Factored Marginal Trajectory Prediction to Interactive Prediction [Notes] CVPR 2022

2022-10 (1)

Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe [Notes] [PJLab]

2022-09 (3)

ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries [Notes] [BEV, perception + prediction, Hang Zhao]
MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction [Notes] [Horizon, BEVNet]
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving ICRA 2022
MOTR: End-to-End Multiple-Object Tracking with Transformer ECCV 2022 [Megvii, MOT]
Anchor DETR: Query Design for Transformer-Based Object Detection [Notes] AAAI 2022 [Megvii]

2022-08 (1)

HOME: Heatmap Output for future Motion Estimation [Notes] ITSC 2021 [behavior prediction, Huawei Paris]

2022-07 (8)

PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark [Notes] [BEVNet, lane line]
VectorMapNet: End-to-end Vectorized HD Map Learning [Notes] [BEVNet, LLD, Hang Zhao]
PETR: Position Embedding Transformation for Multi-View 3D Object Detection [Notes] ECCV 2022 [BEVNet]
PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images [Notes] [BEVNet, MegVii]
M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation [Notes] [BEVNet, nvidia]
BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection [Notes] [BEVNet, NuScenes SOTA, Megvii]
CVT: Cross-view Transformers for real-time Map-view Semantic Segmentation [Notes] CVPR 2022 oral [UTAustin, Philipp]
Wayformer: Motion Forecasting via Simple & Efficient Attention Networks [Notes] [Behavior prediction, Waymo]

2022-06 (3)

BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection [Notes] [BEVNet]
BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving [Notes] [Jiwen Lu, BEVNet, perception + prediction]
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation [Notes] [BEVNet, Han Song]

2022-03 (1)

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers [Notes] ECCV 2022 [BEVNet, Hongyang Li, Jifeng Dai]

2022-02 (1)

TNT: Target-driveN Trajectory Prediction [Notes] CoRL 2020 [prediction, Waymo, Hang Zhao]
DenseTNT: End-to-end Trajectory Prediction from Dense Goal Sets [Notes] ICCV 2021 [prediction, Waymo, 1st place winner WOMD]

2022-01 (1)

Manydepth: The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth [Notes] CVPR 2021 [monodepth, Niantic]
DEKR: Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression [Notes] CVPR 2021

2021-12 (5)

BN-FFN-BN: Leveraging Batch Normalization for Vision Transformers [Notes] ICCVW 2021 [BN, transformers]
PowerNorm: Rethinking Batch Normalization in Transformers [Notes] ICML 2020 [BN, transformers]
MultiPath++: Efficient Information Fusion and Trajectory Aggregation for Behavior Prediction [Notes] ICRA 2022 [Waymo, behavior prediction]
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View [Notes]
Translating Images into Maps [Notes] ICRA 2022 [BEVNet, transformers]

2021-11 (4)

DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [Notes] CoRL 2021 [BEVNet, transformers]
Robust-CVD: Robust Consistent Video Depth Estimation CVPR 2021 oral [website]
MAE: Masked Autoencoders Are Scalable Vision Learners [Notes] [Kaiming He, unsupervised learning]
SimMIM: A Simple Framework for Masked Image Modeling [Notes] [MSRA, unsupervised learning, MAE]
iBOT: Image BERT Pre-Training with Online Tokenizer

2021-10 (3)

STSU: Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images [Notes] ICCV 2021 [BEV feat stitching, Luc Van Gool]
PanopticBEV: Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images [Notes] RAL 2022 [BEVNet, vertical/horizontal features]
NEAT: Neural Attention Fields for End-to-End Autonomous Driving [Notes] ICCV 2021 [supplementary] [BEVNet]

2021-09 (11)

DD3D: Is Pseudo-Lidar needed for Monocular 3D Object detection? [Notes] ICCV 2021 [mono3D, Toyota]
EfficientDet: Scalable and Efficient Object Detection [Notes] CVPR 2020 [BiFPN, Tesla AI day]
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop [Notes] CVPR 2020 [Uber ATG]
MP3: A Unified Model to Map, Perceive, Predict and Plan [Notes] CVPR 2021 [Uber, planning]
BEV-Net: Assessing Social Distancing Compliance by Joint People Localization and Geometric Reasoning [Notes] ICCV 2021 [BEVNet, surveillance]
LiDAR R-CNN: An Efficient and Universal 3D Object Detector [Notes] CVPR 2021 [TuSimple, Naiyan Wang]
Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches [Notes] [corner cases]
Systematization of Corner Cases for Visual Perception in Automated Driving [Notes] IV 2020 [corner cases]
An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving [Notes] IV 2021 [corner cases]
PYVA: Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation [Notes] CVPR 2021 [Supplementary] [BEVNet]
YOLOF: You Only Look One-level Feature [Notes] CVPR 2021 [megvii]
Perceiving Humans: from Monocular 3D Localization to Social Distancing [Notes] TITS 2021 [monoloco++]
PifPaf: Composite Fields for Human Pose Estimation CVPR 2019
Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images [BEVNet]
TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation CVPR 2021
Multi-Modal Fusion Transformer for End-to-End Autonomous Driving CVPR 2021
Conditional DETR for Fast Training Convergence
Probabilistic and Geometric Depth: Detecting Objects in Perspective CoRL 2021

2021-08 (11)

EgoNet: Exploring Intermediate Representation for Monocular Vehicle Pose Estimation [Notes] CVPR 2021 [mono3D]
MonoEF: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach [Notes] CVPR 2021 [mono3D]
GAC: Ground-aware Monocular 3D Object Detection for Autonomous Driving [Notes] RAL 2021 [mono3D]
FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [Notes] NeurIPS 2020 [mono3D, senseTime]
GUPNet: Geometry Uncertainty Projection Network for Monocular 3D Object Detection [Notes] ICCV 2021 [mono3D, Wanli Ouyang]
DARTS: Differentiable Architecture Search [Notes] ICLR 2019 [VGG author]
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search [Notes] CVPR 20219 [DARTS]
FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions CVPR 2020
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining CVPR 2021
Perceiver: General Perception with Iterative Attention [Notes] ICML 2021 [transformers, multimodal]
Perceiver IO: A General Architecture for Structured Inputs & Outputs [Notes]
PillarMotion: Self-Supervised Pillar Motion Learning for Autonomous Driving [Notes] CVPR 2021 [Qcraft, Alan Yuille]
SimTrack: Exploring Simple 3D Multi-Object Tracking for Autonomous Driving [Notes] ICCV 2019 [QCraft, Alan Yuille]

2021-07 (1)

HDMapNet: An Online HD Map Construction and Evaluation Framework [Notes] CVPR 2021 workshop [youtube video only, Li Auto]

2021-06 (2)

FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras [Notes] ICCV 2021 [BEVNet, perception + prediction]
Baidu's CNN seg [Notes]

2021-04 (5)

Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation [Notes] CVPR 2021 [megvii]
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark CVPR 2019
The Overlooked Elephant of Object Detection: Open Set WACV 2021
Class-Agnostic Object Detection WACV 2021
OWOD: Towards Open World Object Detection [Notes] CVPR 2021 oral
FsDet: Frustratingly Simple Few-Shot Object Detection ICML 2020
MonoFlex: Objects are Different: Flexible Monocular 3D Object Detection [Notes] CVPR 2021 [mono3D, Jiwen Lu, cropped]
monoDLE: Delving into Localization Errors for Monocular 3D Object Detection [Notes] CVPR 2021 [mono3D]
Exploring 2D Data Augmentation for 3D Monocular Object Detection
OCM3D: Object-Centric Monocular 3D Object Detection [mono3D]
FSM: Full Surround Monodepth from Multiple Cameras [Notes] ICRA 2021 [monodepth, Xnet]

2021-03 (4)

CaDDN: Categorical Depth Distribution Network for Monocular 3D Object Detection [Notes] CVPR 2021 oral [mono3D, BEVNet]
DSNT: Numerical Coordinate Regression with Convolutional Neural Networks [Notes] [differentiable spatial to numerical transform]
Soft-Argmax: Human pose regression by combining indirect part detection and contextual information
INSTA-YOLO: Real-Time Instance Segmentation [Notes] ICML workshop 2020 [single stage instance segmentation]
CenterNet2: Probabilistic two-stage detection [Notes] [CenterNet, two-stage]

2021-01 (7)

Confluence: A Robust Non-IoU Alternative to Non-Maxima Suppression in Object Detection [Notes] [NMS]
BoxInst: High-Performance Instance Segmentation with Box Annotations [Notes] CVPR 2021 [Chunhua Shen, Tian Zhi]
3DSSD: Point-based 3D Single Stage Object Detector [Notes] CVPR 2020
RepVGG: Making VGG-style ConvNets Great Again [Notes] [Megvii, Xiangyu Zhang, ACNet]
ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks [Notes] ICCV 2019
BEV-Feat-Stitching: Understanding Bird's-Eye View Semantic HD-Maps Using an Onboard Monocular Camera [Notes] [BEVNet, mono3D, Luc Van Gool]
PSS: Object Detection Made Simpler by Eliminating Heuristic NMS [Notes] [Transformer, DETR]

2020-12 (17)

DeFCN: End-to-End Object Detection with Fully Convolutional Network [Notes] [Transformer, DETR]
OneNet: End-to-End One-Stage Object Detection by Classification Cost [Notes] [Transformer, DETR]
Traffic Light Mapping, Localization, and State Detection for Autonomous Vehicles [Notes] ICRA 2011 [traffic light, Sebastian Thrun]
Towards lifelong feature-based mapping in semi-static environments [Notes] ICRA 2016
How to Keep HD Maps for Automated Driving Up To Date [Notes] ICRA 2020 [BMW]
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection [Notes] CVPR 2021 [focal loss]
Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning [Notes] CVPR 2018 workshop
Centroid Voting: Object-Aware Centroid Voting for Monocular 3D Object Detection [Notes] IROS 2020 [mono3D, geometry + appearance = distance]
Monocular 3D Object Detection in Cylindrical Images from Fisheye Cameras [Notes] [GM Israel, mono3D]
DeepPS: Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset TIP 2018 [Parking slot detection, PS2.0 dataset]
PSDet: Efficient and Universal Parking Slot Detection [Notes] IV 2020 [Zongmu, Parking slot detection]
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [Notes] ASPLOS 2020 [pruning]
Scaled-YOLOv4: Scaling Cross Stage Partial Network [Notes] [yolo]
Yolov5 by Ultralytics [Notes] [yolo, spatial2channel]
PP-YOLO: An Effective and Efficient Implementation of Object Detector [Notes] [yolo, paddle-paddle, baidu]
PointPainting: Sequential Fusion for 3D Object Detection [Notes] [nuscenece]
MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps [Notes] CVPR 2020 [Unseen moving objects, BEV]
Locating Objects Without Bounding Boxes [Notes] CVPR 2019 [weighted Haussdorf distance, NMS-free]

2020-11 (18)

TSP: Rethinking Transformer-based Set Prediction for Object Detection [Notes] ICCV 2021 [DETR, transformers, Kris Kitani]
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals [Notes] CVPR 2020 [DETR, Transformer]
Unsupervised Monocular Depth Learning in Dynamic Scenes [Notes] CoRL 2020 [LearnK improved ver, Google]
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time [Notes] ICML 2020 [Mono3D, pairwise relationship]
Argoverse: 3D Tracking and Forecasting with Rich Maps [Notes] CVPR 2019 [HD maps, dataset, CV lidar]
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes [Notes] ICRA 2019
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection CVPRW 2020 [dataset, Daimler, mono3D]
NYC3DCars: A Dataset of 3D Vehicles in Geographic Context ICCV 2013
Towards Fully Autonomous Driving: Systems and Algorithms IV 2011
Center3D: Center-based Monocular 3D Object Detection with Joint Depth Understanding [Notes] [mono3D, LID+DepJoint]
ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection AAAI 2020 oral [mono3D]
CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection [Notes] WACV 2021 [early fusion, camera, radar]
3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation [Notes] NeurIPS 2020 workshop [GM Israel, 3D LLD]
LSTR: End-to-end Lane Shape Prediction with Transformers [Notes] WACV 2021 [LLD, transformers]
PIXOR: Real-time 3D Object Detection from Point Clouds [Notes] CVPR 2018 (birds eye view)
HDNET/PIXOR++: Exploiting HD Maps for 3D Object Detection [Notes] CoRL 2018
CPNDet: Corner Proposal Network for Anchor-free, Two-stage Object Detection ECCV 2020 [anchor free, two stage]
MVF: End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds [Notes] CoRL 2019 [Waymo, VoxelNet 1st author]
Pillar-based Object Detection for Autonomous Driving [Notes] ECCV 2020
Training-Time-Friendly Network for Real-Time Object Detection AAAI 2020 [anchor-free, fast training]
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies [Review of autonomous stack, Yu Huang]
Dense Monocular Depth Estimation in Complex Dynamic Scenes CVPR 2016
Probabilistic Future Prediction for Video Scene Understanding
AB3D: A Baseline for 3D Multi-Object Tracking IROS 2020 [3D MOT]
Spatial-Temporal Relation Networks for Multi-Object Tracking ICCV 2019 [MOT, feature location over time]
Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking ICRA 2018 [MOT, IIT, 3D shape]
ST-3D: Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking CVPR 2020 [Peilinag LI, author of VINS and S3DOT]
Augment Your Batch: Improving Generalization Through Instance Repetition CVPR 2020
RetinaTrack: Online Single Stage Joint Detection and Tracking CVPR 2020 [MOT]
Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots
Gradient Centralization: A New Optimization Technique for Deep Neural Networks ECCV 2020 oral
Depth Completion via Deep Basis Fitting WACV 2020
BTS: From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation [monodepth, supervised]
The Edge of Depth: Explicit Constraints between Segmentation and Depth CVPR 2020 [monodepth, Xiaoming Liu]
On the Continuity of Rotation Representations in Neural Networks CVPR 2019 [rotational representation]
VDO-SLAM: A Visual Dynamic Object-aware SLAM System IJRR 2020
Dynamic SLAM: The Need For Speed
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction ECCV 2020
Traffic Light Mapping and Detection [Notes] ICRA 2011 [traffic light, Google, Chris Urmson]
Traffic light recognition exploiting map and localization at every stage [Notes] Expert Systems 2017 [traffic light, 鲜于明镐，徐在圭，郑浩奇]
Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars [Notes] IJCNN 2019 [traffic light, Espirito Santo Brazil]

2020-10 (14)

TSM: Temporal Shift Module for Efficient Video Understanding [Notes] ICCV 2019 [Song Han, video, object detection]
WOD: Waymo Dataset: Scalability in Perception for Autonomous Driving: Waymo Open Dataset [Notes] CVPR 2020
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [Notes] NeurIPS 2020 [classification as regression]
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection NeurIPS 2020 spotlight
Rethinking the Value of Labels for Improving Class-Imbalanced Learning NeurIPS 2020
RepLoss: Repulsion Loss: Detecting Pedestrians in a Crowd [Notes] CVPR 2018 [crowd detection, Megvii]
Adaptive NMS: Refining Pedestrian Detection in a Crowd [Notes] CVPR 2019 oral [crowd detection, NMS]
AggLoss: Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd [Notes] ECCV 2018 [crowd detection]
CrowdDet: Detection in Crowded Scenes: One Proposal, Multiple Predictions [Notes] CVPR 2020 oral [crowd detection, Megvii, Earth mover's distance]
R2-NMS: NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing [Notes] CVPR 2020
Double Anchor R-CNN for Human Detection in a Crowd [Notes] [head-body bundle]
Review: AP vs MR
SKU110K: Precise Detection in Densely Packed Scenes [Notes] CVPR 2019 [crowd detection, no occlusion]
GossipNet: Learning non-maximum suppression CVPR 2017
TLL: Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation ECCV 2018
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels GCPR 2020 [mono3D, Daniel Cremers, TUM]
CubifAE-3D: Monocular Camera Space Cubification on Autonomous Vehicles for Auto-Encoder based 3D Object Detection [Notes] [mono3D, depth AE pretraining]
Deformable DETR: Deformable Transformers for End-to-End Object Detection [Notes] ICLR 2021 [Jifeng Dai, DETR]
ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale [Notes] ICLR 2021
BYOL: Bootstrap your own latent: A new approach to self-supervised Learning [self-supervised]

2020-09 (15)

SDFLabel: Autolabeling 3D Objects With Differentiable Rendering of SDF Shape Priors [Notes] CVPR 2020 oral [TRI, differentiable rendering]
DensePose: Dense Human Pose Estimation In The Wild [Notes] CVPR 2018 oral [FAIR]
NOCS: Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation CVPR 2019
monoDR: Monocular Differentiable Rendering for Self-Supervised 3D Object Detection [Notes] ECCV 2020 [TRI, mono3D]
Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D [Notes] ECCV 2020 [BEV-Net, Utoronto, Sanja Fidler]
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting ECCV 2020 [Uber ATG, Rachel Urtasun]
FISHING Net: Future Inference of Semantic Heatmaps In Grids [Notes] CVPRW 2020 [BEV-Net, Mapping, Zoox]
VPN: Cross-view Semantic Segmentation for Sensing Surroundings [Notes] RAL 2020 [Bolei Zhou, BEV-Net]
VED: Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks [Notes] ICRA 2019 [BEV-Net]
Cam2BEV: A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View [Notes] ITSC 2020 [BEV-Net]
Learning to Look around Objects for Top-View Representations of Outdoor Scenes [Notes] ECCV 2018 [BEV-Net, UCSD, Manmohan Chandraker]
A Parametric Top-View Representation of Complex Road Scenes CVPR 2019 [BEV-Net, UCSD, Manmohan Chandraker]
FTM: Understanding Road Layout from Videos as a Whole CVPR 2020 [BEV-Net, UCSD, Manmohan Chandraker]
KM3D-Net: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training [Notes] RAL 2021 [RTM3D, Peixuan Li]
InstanceMotSeg: Real-time Instance Motion Segmentation for Autonomous Driving [Notes] IROS 2020 [motion segmentation]
MPV-Nets: Monocular Plan View Networks for Autonomous Driving [Notes] IROS 2019 [BEV-Net]
Class-Balanced Loss Based on Effective Number of Samples [Notes] CVPR 2019 [Focal loss authors]
Geometric Pretraining for Monocular Depth Estimation [Notes] ICRA 2020
Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving [Notes] Sensors 2020 [traffic light, 金沢]

2020-08 (26)

Feature-metric Loss for Self-supervised Learning of Depth and Egomotion [Notes] ECCV 2020 [feature-metric, local minima, monodepth]
Depth-VO-Feat: Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction CVPR 2018 [feature-metric, monodepth]
MonoResMatch: Learning monocular depth estimation infusing traditional stereo knowledge [Notes] CVPR 2019 [monodepth, local minima, cheap stereo GT]
SGDepth: Self-Supervised Monocular Depth Estimation: Solving the Dynamic Object Problem by Semantic Guidance [Notes] ECCV 2020 [Moving objects]
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding ECCV 2018 [dynamic objects, rigid and dynamic motion]
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding TPAMI 2018
CC: Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation [Notes] CVPR 2019
ObjMotionNet: Self-supervised Object Motion and Depth Estimation from Video [Notes] CVPRW 2020 [object motion prediction, velocity prediction]
Instance-wise Depth and Motion Learning from Monocular Videos
Semantics-Driven Unsupervised Learning for Monocular Depth and Ego-Motion Estimation
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency ECCV 2018
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments [mapping]
Road-SLAM: Road Marking based SLAM with Lane-level Accuracy [Notes] [HD mapping]
AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot [Notes] IROS 2020 [Huawei, HD mapping, Tong Qin, VINS author, autonomous valet parking]
AVP-SLAM-Late-Fusion: Mapping and Localization using Semantic Road Marking with Centimeter-level Accuracy in Indoor Parking Lots [Notes] ITSC 2019
Lane markings-based relocalization on highway ITSC 2019
DeepRoadMapper: Extracting Road Topology from Aerial Images [Notes] ICCV 2017 [Uber ATG, NOT HD maps]
RoadTracer: Automatic Extraction of Road Networks from Aerial Images CVPR 2018 [NOT HD maps]
PolyMapper: Topological Map Extraction From Overhead Images [Notes] ICCV 2019 [mapping, polygon, NOT HD maps]
HRAN: Hierarchical Recurrent Attention Networks for Structured Online Maps [Notes] CVPR 2018 [HD mapping, highway, polyline loss, Chamfer distance]
Deep Structured Crosswalk: End-to-End Deep Structured Models for Drawing Crosswalks [Notes] ECCV 2018
DeepBoundaryExtractor: Convolutional Recurrent Network for Road Boundary Extraction [Notes] CVPR 2019 [HD mapping, boundary, polyline loss]
DAGMapper: Learning to Map by Discovering Lane Topology [Notes] ICCV 2019 [HD mapping, highway, forks and merges, polyline loss]
Sparse-HD-Maps: Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization [Notes] IROS 2019 oral [Uber ATG, metadata, mapping, localization]
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks IEEE TGRS 2018
Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs Sensors 2020 [Tsinghua, 3D HD maps]
PatchNet: Rethinking Pseudo-LiDAR Representation [Notes] ECCV 2020 [SenseTime, Wanli Ouyang]
D4LCN: Learning Depth-Guided Convolutions for Monocular 3D Object Detection [Notes] CVPR 2020 [mono3D]
MfS: Learning Stereo from Single Images [Notes] ECCV 2020 [mono for stereo, learn stereo matching with mono]
BorderDet: Border Feature for Dense Object Detection ECCV 2020 oral [Megvii]
Scale-Aware Trident Networks for Object Detection ICCV 2019 [different heads for different scales]
Learning Depth from Monocular Videos using Direct Methods
Vid2Depth: Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints CVPR 2018 [Google]
NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Supervising the new with the old: learning SFM from SFM [Notes] ECCV 2018
Neural RGB->D Sensing: Depth and Uncertainty from a Video Camera CVPR 2019 [multi-frame monodepth]
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video [multi-frame monodepth, RNN]
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth [multi-frame monodepth, RNN]
Exploiting temporal consistency for real-time video depth estimation ICCV 2019 [multi-frame monodepth, RNN, indoor]
SfM-Net: Learning of Structure and Motion from Video [dynamic object, SfM]
MB-Net: MergeBoxes for Real-Time 3D Vehicles Detection [Notes] IV 2018 [mono3D: Daimler]
BS3D: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images [Notes] IV 2019 [mono3D, Daimler]
3D-GCK: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time [Notes] IV 2020 [[mono3D, Daimler]
UR3D: Distance-Normalized Unified Representation for Monocular 3D Object Detection [Notes] ECCV 2020 [mono3D]
DA-3Det: Monocular 3D Object Detection via Feature Domain Adaptation [Notes] ECCV 2020 [mono3D]
RAR-Net: Reinforced Axial Refinement Network for Monocular 3D Object Detection [Notes] ECCV 2020 [mono3D]

2020-07 (25)

CenterTrack: Tracking Objects as Points [Notes] ECCV 2020 spotlight [camera based 3D MOD, MOT SOTA, CenterNet, video based object detection, Philipp Krähenbühl]
CenterPoint: Center-based 3D Object Detection and Tracking [Notes] CVPR 2021 [lidar based 3D MOD, CenterNet]
Tracktor: Tracking without bells and whistles [Notes] ICCV 2019 [Tracktor/Tracktor++, Laura Leal-Taixe@TUM]
FairMOT: A Simple Baseline for Multi-Object Tracking [Notes]
DeepMOT: A Differentiable Framework for Training Multiple Object Trackers [Notes] CVPR 2020 [trainable Hungarian, Laura Leal-Taixe@TUM]
MPNTracker: Learning a Neural Solver for Multiple Object Tracking CVPR 2020 oral [trainable Hungarian, Laura Leal-Taixe@TUM]
nuScenes: A multimodal dataset for autonomous driving [Notes] CVPR 2020 [dataset, point cloud, radar]
CBGS: Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection [Notes] CVPRW 2019 [Megvii, lidar, WAD challenge winner]
AFDet: Anchor Free One Stage 3D Object Detection and Competition solution [Notes] CVPRW 2020 [Horizon robotics, lidar, winning for Waymo challenge]
Review of MOT and SOT [Notes]
CrowdHuman: A Benchmark for Detecting Human in a Crowd [Notes] [megvii, pedestrian, dataset]
WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild [Notes] TMM 2019 [dataset, pedestrian]
Tsinghua-Daimler Cyclists: A New Benchmark for Vison-Based Cyclist Detection [Notes] IV 2016 [dataset, cyclist Detection]
Specialized Cyclist Detection Dataset: Challenging Real-World Computer Vision Dataset for Cyclist Detection Using a Monocular RGB Camera [Notes] IV 2019 [Extention to KITTI]
PointTrack: Segment as Points for Efficient Online Multi-Object Tracking and Segmentation [Notes] ECCV 2020 oral [MOTS]
PointTrack++ for Effective Online Multi-Object Tracking and Segmentation [Notes] CVPR 2020 workshop [CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS]
SpatialEmbedding: Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth [Notes] ICCV 2019 [one-stage, instance segmentation]
BA-Net: Dense Bundle Adjustment Networks [Notes] ICLR 2019 [Bundle adjustment, multi-frame monodepth, feature-metric]
DeepSFM: Structure From Motion Via Deep Bundle Adjustment ECCV 2020 oral [multi-frame monodepth, indoor scene]
CVD: Consistent Video Depth Estimation [Notes] SIGGRAPH 2020 [multi-frame monodepth, online finetune]
DeepV2D: Video to Depth with Differentiable Structure from Motion [Notes] ICLR 2020 [multi-frame monodepth, Jia Deng]
GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose [Notes] CVPR 2018 [residual optical flow, monodepth, rigid and dynamic motion]
GLNet: Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera [Notes] ICCV 2019 [online finetune, rigid and dynamic motion]
Depth Hints: Self-Supervised Monocular Depth Hints [Notes] ICCV 2019 [monodepth, local minima, cheap stereo GT]
MonoUncertainty: On the uncertainty of self-supervised monocular depth estimation [Notes] CVPR 2020 [depth uncertainty]
Self-Supervised Learning of Depth and Ego-motion with Differentiable Bundle Adjustment [Notes] [Bundle adjustment, xmotors.ai, multi-frame monodepth]
Kinematic 3D Object Detection in Monocular Video [Notes] ECCV 2020 [multi-frame mono3D, Xiaoming Liu]
VelocityNet: Camera-based vehicle velocity estimation from monocular video [Notes] CVPR 2017 workshop [monocular velocity estimation, CVPR 2017 challenge winner]
Vehicle Centric VelocityNet: End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [Notes] [monocular velocity estimation, monocular distance, SOTA]

2020-06 (20)

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain [Notes] IROS 2018 [lidar, mapping]
PIE: A Large-Scale Dataset and Models for Pedestrian Intention Estimation and Trajectory Prediction [Notes] ICCV 2019
JAAD: Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior ICCV 2017
Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs BMVC 2019
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation IV 2018
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation ITSC 2019 [skeleton, pedestrian, cyclist intention]
Attentive Single-Tasking of Multiple Tasks CVPR 2019
DETR: End-to-End Object Detection with Transformers [Notes] ECCV 2020 oral [FAIR]
Transformer: Attention Is All You Need [Notes] NIPS 2017
SpeedNet: Learning the Speediness in Videos [Notes] CVPR 2020 oral
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships [Notes] CVPR 2020 [Mono3D, pairwise relationship]
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation [Notes] CVPRW 2020 [Mono3D, Zongmu]
Vehicle Re-ID for Surround-view Camera System [Notes] CVPRW 2020 [tireline, vehicle ReID, Zongmu]
End-to-End Lane Marker Detection via Row-wise Classification [Notes] [Qualcomm Korea, LLD as cls]
Reliable multilane detection and classification by utilizing CNN as a regression network ECCV 2018 [LLD as reg]
SUPER: A Novel Lane Detection System [Notes]
Learning Lightweight Lane Detection CNNs by Self Attention Distillation ICCV 2019
StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation BMVC 2015
StixelNetV2: Real-time category-based and general obstacle detection for autonomous driving [Notes] ICCV 2017 [DS]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [Notes] CVPR 2016 [channel-to-pixel]
Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints [mono3D]
Self-Mono-SF: Self-Supervised Monocular Scene Flow Estimation [Notes] CVPR 2020 oral [scene-flow, Stereo input]
MEBOW: Monocular Estimation of Body Orientation In the Wild [Notes] CVPR 2020
VG-NMS: Visibility Guided NMS: Efficient Boosting of Amodal Object Detection in Crowded Traffic Scenes [Notes] NeurIPS 2019 workshop [Crowded scene, NMS, Daimler]
WYSIWYG: What You See is What You Get: Exploiting Visibility for 3D Object Detection [Notes] CVPR 2020 oral [occupancy grid]
Real-Time Panoptic Segmentation From Dense Detections [Notes] CVPR 2020 oral [bbox + semantic segmentation = panoptic segmentation, Toyota]
Human-Centric Efficiency Improvements in Image Annotation for Autonomous Driving [Notes] CVPRW 2020 [efficient annotation]
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving [Notes] CVPR 2020 oral [Waymo, auto data generation, surfel]
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World [Notes] CVPR 2020 oral [Uber ATG, auto data generation, surfel]
SuMa++: Efficient LiDAR-based Semantic SLAM IROS 2019 [semantic segmentation, lidar, SLAM]
PON/PyrOccNet: Predicting Semantic Map Representations from Images using Pyramid Occupancy Networks [Notes] CVPR 2020 oral [BEV-Net, OFT]
MonoLayout: Amodal scene layout from a single image [Notes] WACV 2020 [BEV-Net]
BEV-Seg: Bird’s Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud [Notes] CVPR 2020 workshop [BEV-Net, Mapping]
A Geometric Approach to Obtain a Bird's Eye View from an Image ICCVW 2019 [mapping, geometry, Andrew Zisserman]
FrozenDepth: Learning the Depths of Moving People by Watching Frozen People [Notes] CVPR 2019 oral
ORB-SLAM: a Versatile and Accurate Monocular SLAM System TRO 2015
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras TRO 2016
CubeSLAM: Monocular 3D Object SLAM [Notes] TRO 2019 [dynamic SLAM, orb slam + mono3D]
ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundings [Notes] CVPR 2020 [general dynamic SLAM]
S3DOT: Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving [Notes] ECCV 2018 [Peiliang Li]
Multi-object Monocular SLAM for Dynamic Environments [Notes] IV 2020 [monolayout authors]
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume [Notes] CVPR 2018 oral [Optical flow]
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation CVPR 2018 [Optical flow]
FlowNet: Learning Optical Flow With Convolutional Networks ICCV 2015 [Optical flow]
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks CVPR 2017 [Optical flow]
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network CVPR 2019 [semantic segmentation, lightweight]
Mono-SF: Multi-View Geometry Meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes ICCV 2019 [depth uncertainty]

2020-05 (19)

Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems [Notes] [Honda] ICRA 2019
PackNet: 3D Packing for Self-Supervised Monocular Depth Estimation [Notes] CVPR 2020 oral [Scale aware depth]
PackNet-SG: Semantically-Guided Representation Learning for Self-Supervised Monocular Depth [Notes] ICLR 2020 [TRI, infinite-depth problem]
TrianFlow: Towards Better Generalization: Joint Depth-Pose Learning without PoseNet [Notes] CVPR 2020 [Scale aware]
Understanding the Limitations of CNN-based Absolute Camera Pose Regression [Notes] CVPR 2019 [Drawbacks of PoseNet, MapNet, Laura Leal-Taixe@TUM]
To Learn or Not to Learn: Visual Localization from Essential Matrices [Notes] ICRA 2020 [SIFT + 5 pt solver >> others for VO, Laura Leal-Taixe@TUM]
DF-VO: Visual Odometry Revisited: What Should Be Learnt? [Notes] ICRA 2020 [Depth and Flow for accurate VO]
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry [Notes] CVPR 2020 oral [Daniel Cremers, TUM, depth uncertainty]
Network Slimming: Learning Efficient Convolutional Networks through Network Slimming [Notes] ICCV 2017
BatchNorm Pruning: Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers [Notes] ICLR 2018
Direct Sparse Odometry PAMI 2018
Train in Germany, Test in The USA: Making 3D Object Detectors Generalize [Notes] CVPR 2020
PseudoLidarV3: End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection [Notes] CVPR 2020
ATSS: Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection [Notes] CVPR 2020 oral
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression AAAI 2020
Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation [Journal version]
YOLOv4: Optimal Speed and Accuracy of Object Detection [Notes]
CBN: Cross-Iteration Batch Normalization [Notes]
Stitcher: Feedback-driven Data Provider for Object Detection [Notes]
SKNet: Selective Kernel Networks [Notes] CVPR 2019
CBAM: Convolutional Block Attention Module [Notes] ECCV 2018
ResNeSt: Split-Attention Networks [Notes]

2020-04 (14)

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst [Notes] RSS 2019 [Waymo]
IntentNet: Learning to Predict Intention from Raw Sensor Data [Notes] CoRL 2018 [Uber ATG, perception and prediction, Lidar+Map]
RoR: Rules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions [Notes] CVPR 2019 [Zoox]
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction [Notes] CoRL 2019 [Waymo, authors from RoR and ChauffeurNet]
NMP: End-to-end Interpretable Neural Motion Planner [Notes] CVPR 2019 oral [Uber ATG]
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks [Notes] ICRA 2019 [Henggang Cui, Multimodal, Uber ATG Pittsburgh]
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving WACV 2020 [Uber ATG Pittsburgh]
TensorMask: A Foundation for Dense Object Segmentation [Notes] ICCV 2019 [single-stage instance seg]
BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation [Notes] CVPR 2020 oral
Mask Encoding for Single Shot Instance Segmentation [Notes] CVPR 2020 oral [single-stage instance seg, Chunhua Shen]
PolarMask: Single Shot Instance Segmentation with Polar Representation [Notes] CVPR 2020 oral [single-stage instance seg]
SOLO: Segmenting Objects by Locations [Notes] ECCV 2020 [single-stage instance seg, Chunhua Shen]
SOLOv2: Dynamic, Faster and Stronger [Notes] [single-stage instance seg, Chunhua Shen]
CondInst: Conditional Convolutions for Instance Segmentation [Notes] ECCV 2020 oral [single-stage instance seg, Chunhua Shen]
CenterMask: Single Shot Instance Segmentation With Point Representation [Notes]CVPR 2020

2020-03 (15)

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition [Notes] ICCV 2017
Which Tasks Should Be Learned Together in Multi-task Learning? [Notes] [Stanford, MTL] ICML 2020
MGDA: Multi-Task Learning as Multi-Objective Optimization NeurIPS 2018
Taskonomy: Disentangling Task Transfer Learning [Notes] CVPR 2018
Rethinking ImageNet Pre-training [Notes] ICCV 2019 [Kaiming He]
UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor [Notes] [superpoint]
KP2D: Neural Outlier Rejection for Self-Supervised Keypoint Learning [Notes] ICLR 2020 (pointNet)
KP3D: Self-Supervised 3D Keypoint Learning for Ego-motion Estimation [Notes] CoRL 2020 [Toyota, superpoint]
NG-RANSAC: Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses [Notes] ICCV 2019 [pointNet]
Learning to Find Good Correspondences [Notes] CVPR 2018 Oral (pointNet)
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving [Notes] [Huawei, Mono3D]
DSP: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation [Notes] AAAI 2020 (SenseTime, Mono3D)
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks (LLD, LSTM)
LaneNet: Towards End-to-End Lane Detection: an Instance Segmentation Approach [Notes] IV 2018 (LaneNet)
3D-LaneNet: End-to-End 3D Multiple Lane Detection [Notes] ICCV 2019
Semi-Local 3D Lane Detection and Uncertainty Estimation [Notes] [GM Israel, 3D LLD]
Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection [Notes] ECCV 2020 [Apollo, 3D LLD]
Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty CVPR 2018 [Egocentric prediction]
It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection ECCV 2018 [pedestrian]

2020-02 (12)

Associative Embedding: End-to-End Learning for Joint Detection and Grouping [Notes] NIPS 2017
Pixels to Graphs by Associative Embedding [Notes] NIPS 2017
Social LSTM: Human Trajectory Prediction in Crowded Spaces [Notes] CVPR 2017
Online Video Object Detection using Association LSTM [Notes] [single stage, recurrent]
SuperPoint: Self-Supervised Interest Point Detection and Description [Notes] CVPR 2018 (channel-to-pixel, deep SLAM, Magic Leap)
PointRend: Image Segmentation as Rendering [Notes] CVPR 2020 Oral [Kaiming He, FAIR]
Multigrid: A Multigrid Method for Efficiently Training Video Models [Notes] CVPR 2020 Oral [Kaiming He, FAIR]
GhostNet: More Features from Cheap Operations [Notes] CVPR 2020
FixRes: Fixing the train-test resolution discrepancy [Notes] NIPS 2019 [FAIR]
MoVi-3D: Towards Generalization Across Depth for Monocular 3D Object Detection [Notes] ECCV 2020 [Virtual Cam, viewport, Mapillary/Facebook, Mono3D]
Amodal Completion and Size Constancy in Natural Scenes [Notes] ICCV 2015 (Amodal completion)
MoCo: Momentum Contrast for Unsupervised Visual Representation Learning [Notes] CVPR 2020 Oral [FAIR, Kaiming He]

2020-01 (19)

Double Descent: Reconciling modern machine learning practice and the bias-variance trade-of [Notes] PNAS 2019
Deep Double Descent: Where Bigger Models and More Data Hurt [Notes]
Visualizing the Loss Landscape of Neural Nets NIPS 2018
The ApolloScape Open Dataset for Autonomous Driving and its Application CVPR 2018 (dataset)
ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving [Notes] CVPR 2019
Part-level Car Parsing and Reconstruction from a Single Street View [Notes] [Baidu]
6D-VNet: End-to-end 6DoF Vehicle Pose Estimation from Monocular RGB Images [Notes] CVPR 2019
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving [Notes] ECCV 2020 spotlight
DORN: Deep Ordinal Regression Network for Monocular Depth Estimation [Notes] CVPR 2018 [monodepth, supervised]
D&T: Detect to Track and Track to Detect [Notes] ICCV 2017 (from Feichtenhofer)
CRF-Net: A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection [Notes] SDF 2019 (radar detection)
RVNet: Deep Sensor Fusion of Monocular Camera and Radar for Image-based Obstacle Detection in Challenging Environments [Notes] PSIVT 2019
RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles [Notes] ICIP 2019
ROLO: Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking [Notes] ISCAS 2016
Recurrent SSD: Recurrent Multi-frame Single Shot Detector for Video Object Detection [Notes] BMVC 2018 (Mitsubishi)
Recurrent RetinaNet: A Video Object Detection Model Based on Focal Loss [Notes] ICONIP 2018 (single stage, recurrent)
Actions as Moving Points [Notes] [not suitable for online]
The PREVENTION dataset: a novel benchmark for PREdiction of VEhicles iNTentIONs [Notes] ITSC 2019 [dataset, cut-in]
Semi-Automatic High-Accuracy Labelling Tool for Multi-Modal Long-Range Sensor Dataset [Notes] IV 2018
Astyx dataset: Automotive Radar Dataset for Deep Learning Based 3D Object Detection [Notes] EuRAD 2019 (Astyx)
Astyx camera radar: Deep Learning Based 3D Object Detection for Automotive Radar and Camera [Notes] EuRAD 2019 (Astyx)

2019-12 (12)

How Do Neural Networks See Depth in Single Images? [Notes] ICCV 2019
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019 (depth completion)
DC: Depth Coefficients for Depth Completion [Notes] CVPR 2019 [Xiaoming Liu, Multimodal]
Parse Geometry from a Line: Monocular Depth Estimation with Partial Laser Observation [Notes] ICRA 2017
VO-Monodepth: Enhancing self-supervised monocular depth estimation with traditional visual odometry [Notes] 3DV 2019 (sparse to dense)
Probabilistic Object Detection: Definition and Evaluation [Notes]
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation [Notes] ICCV 2019
On Calibration of Modern Neural Networks [Notes] ICML 2017 (Weinberger)
Extreme clicking for efficient object annotation [Notes] ICCV 2017
Radar and Camera Early Fusion for Vehicle Detection in Advanced Driver Assistance Systems [Notes] NeurIPS 2019 (radar)
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector [Notes] IV 2019
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion [Notes] ICCV 2019
YOLACT: Real-time Instance Segmentation [Notes] ICCV 2019 [single-stage instance seg]
YOLACT++: Better Real-time Instance Segmentation [single-stage instance seg]

2019-11 (20)

Review of Image and Feature Descriptors
Vehicle Detection With Automotive Radar Using Deep Learning on Range-Azimuth-Doppler Tensors [Notes] ICCV 2019
GPP: Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road [Notes] IV 2020 [UCSD, Trevidi, mono 3DOD]
MVRA: Multi-View Reprojection Architecture for Orientation Estimation [Notes] ICCV 2019
YOLOv3: An Incremental Improvement
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving [Notes] ICCV 2019 (Detection with Uncertainty)
Bayesian YOLOv3: Uncertainty Estimation in One-Stage Object Detection [Notes] [DriveU]
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [Notes] ITSC 2018 (DriveU)
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [Notes] IV 2019 (DriveU)
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving [Notes] IROS 2019 (DriveU)
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [Notes] CVPR 2019 (uncertainty)
LaserNet KL: Learning an Uncertainty-Aware Object Detector for Autonomous Driving [Notes] [LaserNet with KL divergence]
IoUNet: Acquisition of Localization Confidence for Accurate Object Detection [Notes] ECCV 2018
gIoU: Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression [Notes] CVPR 2019
The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks CVPR 2018 [IoU as loss]
KL Loss: Bounding Box Regression with Uncertainty for Accurate Object Detection [Notes] CVPR 2019
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth [Notes] CVPR 2019
BayesOD: A Bayesian Approach for Uncertainty Estimation in Deep Object Detectors [Notes]
TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching [Notes] ICIP 2019
Accurate Uncertainties for Deep Learning Using Calibrated Regression [Notes] ICML 2018
Calibrating Uncertainties in Object Localization Task [Notes] NIPS 2018
SMWA: On the Over-Smoothing Problem of CNN Based Disparity Estimation [Notes] ICCV 2019 [Multimodal, depth estimation]
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image [Notes] ICRA 2018 (depth completion)

2019-10 (18)

Review of monocular object detection
Review of 2D 3D contraints in Mono 3DOD
MonoGRNet 2: Monocular 3D Object Detection via Geometric Reasoning on Keypoints [Notes] [estimates depth from keypoints]
Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image [Notes] CVPR 2017
SS3D: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss [Notes] [rergess distance from images, centernet like]
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving [Notes] CVPR 2019
M3D-RPN: Monocular 3D Region Proposal Network for Object Detection [Notes] ICCV 2019 oral [3D anchors, cyclists, Xiaoming Liu]
TLNet: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection [Notes] CVPR 2019
A Survey on 3D Object Detection Methods for Autonomous Driving Applications [Notes] TITS 2019 [Review]
BEV-IPM: Deep Learning based Vehicle Position and Orientation Estimation via Inverse Perspective Mapping Image [Notes] IV 2019
ForeSeE: Task-Aware Monocular Depth Estimation for 3D Object Detection [Notes] AAAI 2020 oral [successor to pseudo-lidar, mono 3DOD SOTA]
Obj-dist: Learning Object-specific Distance from a Monocular Image [Notes] ICCV 2019 (xmotors.ai + NYU) [monocular distance]
DisNet: A novel method for distance estimation from monocular camera [Notes] IROS 2018 [monocular distance]
BirdGAN: Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles [Notes] IROS 2019
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints [Notes] ICIP 2019
3D-RCNN: Instance-level 3D Object Reconstruction via Render-and-Compare [Notes] CVPR 2018
Deep Optics for Monocular Depth Estimation and 3D Object Detection [Notes] ICCV 2019
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation [Notes] ICCV 2019
Joint Monocular 3D Vehicle Detection and Tracking [Notes] ICCV 2019 (Berkeley DeepDrive)
CasGeo: 3D Bounding Box Estimation for Autonomous Vehicles by Cascaded Geometric Constraints and Depurated 2D Detections Using 3D Results [Notes]

2019-09 (17)

Slimmable Neural Networks [Notes] ICLR 2019
Universally Slimmable Networks and Improved Training Techniques [Notes] ICCV 2019
AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
Once for All: Train One Network and Specialize it for Efficient Deployment
DOTA: A Large-scale Dataset for Object Detection in Aerial Images [Notes] CVPR 2018 (rotated bbox)
RoiTransformer: Learning RoI Transformer for Oriented Object Detection in Aerial Images [Notes] CVPR 2019 (rotated bbox)
RRPN: Arbitrary-Oriented Scene Text Detection via Rotation Proposals TMM 2018
R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection (rotated bbox)
TI white paper: Webinar: mmWave Radar for Automotive and Industrial applications [Notes] [TI, radar]
Federated Learning: Strategies for Improving Communication Efficiency [Notes] NIPS 2016
sort: Simple Online and Realtime Tracking [Notes] ICIP 2016
deep-sort: Simple Online and Realtime Tracking with a Deep Association Metric [Notes]
MT-CNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks [Notes] SPL 2016 (real time, facial landmark)
RetinaFace: Single-stage Dense Face Localisation in the Wild [Notes] CVPR 2020 [joint object and landmark detection]
SC-SfM-Learner: Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video [Notes] NIPS 2019
SiamMask: Fast Online Object Tracking and Segmentation: A Unifying Approach CVPR 2019 (tracking, segmentation, label propagation)
Review of Kálmán Filter (from Tim Babb, Pixar Animation) [Notes]
R-FCN: Object Detection via Region-based Fully Convolutional Networks [Notes] NIPS 2016
Guided backprop: Striving for Simplicity: The All Convolutional Net [Notes] ICLR 2015
Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks [Notes] CVPR 2019
Boxy Vehicle Detection in Large Images [Notes] ICCV 2019
FQNet: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection [Notes] CVPR 2019 [Mono 3DOD, Jiwen Lu]

2019-08 (18)

Mono3D: Monocular 3D Object Detection for Autonomous Driving [Notes] CVPR2016
MonoDIS: Disentangling Monocular 3D Object Detection [Notes] ICCV 2019
Pseudo lidar-e2e: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud [Notes] ICCV 2019 (pseudo-lidar with 2d and 3d consistency loss, better than PL and worse than PL++, SOTA for pure mono3D)
MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization [Notes] AAAI 2019 (SOTA of Mono3DOD, MLF < MonoGRNet < Pseudo-lidar)
MLF: Multi-Level Fusion based 3D Object Detection from Monocular Images [Notes] CVPR 2018 (precursor to pseudo-lidar)
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape [Notes] CVPR 2019
AM3D: Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving [Notes] ICCV 2019 [similar to pseudo-lidar, color-enhanced]
Mono3D++: Monocular 3D Vehicle Detection with Two-Scale 3D Hypotheses and Task Priors [Notes] (from Stefano Soatto) AAAI 2019
Deep Metadata Fusion for Traffic Light to Lane Assignment [Notes] IEEE RA-L 2019 (traffic lights association)
Automatic Traffic Light to Ego Vehicle Lane Association at Complex Intersections ITSC 2019 (traffic lights association)
Distant Vehicle Detection Using Radar and Vision[Notes] ICRA 2019 [radar, vision, radar tracklets fusion]
Distance Estimation of Monocular Based on Vehicle Pose Information [Notes]
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics [Notes] CVPR 2018 (Alex Kendall)
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks [Notes] ICML 2018 (multitask)
DTP: Dynamic Task Prioritization for Multitask Learning [Notes] ECCV 2018 [multitask, Stanford]
Will this car change the lane? - Turn signal recognition in the frequency domain [Notes] IV 2014
Complex-YOLO: Real-time 3D Object Detection on Point Clouds [Notes] (BEV detection only)
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds CVPR 2019 (sensor fusion and tracking)
An intriguing failing of convolutional neural networks and the CoordConv solution [Notes] NIPS 2018

2019-07 (19)

Deep Parametric Continuous Convolutional Neural Networks [Notes] CVPR 2018 (@Uber, sensor fusion)
ContFuse: Deep Continuous Fusion for Multi-Sensor 3D Object Detection [Notes] ECCV 2018 [Uber ATG, sensor fusion, BEV]
Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net [Notes] CVPR 2018 oral [lidar only, perception and prediction]
LearnK: Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras [Notes] ICCV 2019 [monocular depth estimation, intrinsic estimation, SOTA]
monodepth: Unsupervised Monocular Depth Estimation with Left-Right Consistency [Notes] CVPR 2017 oral (monocular depth estimation, stereo for training)
Struct2depth: Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos [Notes] AAAI 2019 [monocular depth estimation, estimating movement of dynamic object, infinite depth problem, online finetune]
Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistency [Notes] AAAI 2018 (monocular depth estimation, static assumption, surface normal)
LEGO Learning Edge with Geometry all at Once by Watching Videos [Notes] CVPR 2018 spotlight (monocular depth estimation, static assumption, surface normal)
Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network [Notes] (radar, RD map, OD, Arxiv 201902)
A study on Radar Target Detection Based on Deep Neural Networks [Notes] (radar, RD map, OD)
2D Car Detection in Radar Data with PointNets [Notes] (from Ulm Univ, radar, point cloud, OD, Arxiv 201904)
Learning Confidence for Out-of-Distribution Detection in Neural Networks [Notes] (budget to cheat)
A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification [Notes] ICRA 2017 (Bosch, traffic lights)
How hard can it be? Estimating the difficulty of visual search in an image [Notes] CVPR 2016
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges [Notes] (review from Bosch)
Review of monocular 3d object detection (blog from 知乎)
Deep3dBox: 3D Bounding Box Estimation Using Deep Learning and Geometry [Notes] CVPR 2017 [Zoox]
MonoPSR: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction [Notes] CVPR 2019
OFT: Orthographic Feature Transform for Monocular 3D Object Detection [Notes] BMVC 2019 [Convert camera to BEV, Alex Kendall]

2019-06 (12)

MixMatch: A Holistic Approach to Semi-Supervised Learning [Notes]
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks [Notes] ICML 2019
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [Notes] NIPS 2017
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding [Notes]BMVC 2017
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents [Notes] AAAI 2019 oral
Deep Depth Completion of a Single RGB-D Image [Notes] CVPR 2018 (indoor)
DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene from Sparse LiDAR Data and Single Color Image [Notes] CVPR 2019 (outdoor)
SfMLearner: Unsupervised Learning of Depth and Ego-Motion from Video [Notes] CVPR 2017
Monodepth2: Digging Into Self-Supervised Monocular Depth Estimation [Notes] ICCV 2019 [Niantic]
DeepSignals: Predicting Intent of Drivers Through Visual Signals [Notes] ICRA 2019 (@Uber, turn signal detection)
FCOS: Fully Convolutional One-Stage Object Detection [Notes] ICCV 2019 [Chunhua Shen]
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving [Notes] ICLR 2020
MMF: Multi-Task Multi-Sensor Fusion for 3D Object Detection [Notes] CVPR 2019 (@Uber, sensor fusion)

2019-05 (18)

CenterNet: Objects as points (from ExtremeNet authors) [Notes]
CenterNet: Object Detection with Keypoint Triplets [Notes]
Object Detection based on Region Decomposition and Assembly [Notes] AAAI 2019
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks [Notes] ICLR 2019
M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network [Notes] AAAI 2019
Deep Radar Detector [Notes] RadarCon 2019
Semantic Segmentation on Radar Point Clouds [[Notes]] (from Daimler AG) FUSION 2018
Pruning Filters for Efficient ConvNets [Notes] ICLR 2017
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks [Notes] NIPS 2018 talk
LeGR: Filter Pruning via Learned Global Ranking [Notes] CVPR 2020 oral
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection [Notes] CVPR 2019
AutoAugment: Learning Augmentation Policies from Data [Notes] CVPR 2019
Path Aggregation Network for Instance Segmentation [Notes] CVPR 2018
Channel Pruning for Accelerating Very Deep Neural Networks ICCV 2017 (Face++, Yihui He) [Notes]
AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV 2018 (Song Han, Yihui He)
MobileNetV3: Searching for MobileNetV3 [Notes] ICCV 2019
MnasNet: Platform-Aware Neural Architecture Search for Mobile [Notes] CVPR 2019
Rethinking the Value of Network Pruning ICLR 2019

2019-04 (12)

MobileNetV2: Inverted Residuals and Linear Bottlenecks (MobileNets v2) [Notes] CVPR 2018
A New Performance Measure and Evaluation Benchmark for Road Detection Algorithms [Notes] ITSC 2013
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving [Notes]
Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction (Very nice illustration of 1 and 2 stage object detection)
Light-Head R-CNN: In Defense of Two-Stage Object Detector [Notes] (from Megvii)
CSP: High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection [Notes] CVPR 2019 [center and scale prediction, anchor-free, near SOTA pedestrian]
Review of Anchor-free methods (知乎Blog) 目标检测：Anchor-Free时代 Anchor free深度学习的目标检测方法 My Slides on CSP
DenseBox: Unifying Landmark Localization with End to End Object Detection
CornerNet: Detecting Objects as Paired Keypoints [Notes] ECCV 2018
ExtremeNet: Bottom-up Object Detection by Grouping Extreme and Center Points [Notes] CVPR 2019
FSAF: Feature Selective Anchor-Free Module for Single-Shot Object Detection [Notes] CVPR 2019
FoveaBox: Beyond Anchor-based Object Detector (anchor-free) [Notes]

2019-03 (19)

Bag of Freebies for Training Object Detection Neural Networks [Notes]
mixup: Beyond Empirical Risk Minimization [Notes] ICLR 2018
Multi-view Convolutional Neural Networks for 3D Shape Recognition (MVCNN) [Notes] ICCV 2015
3D ShapeNets: A Deep Representation for Volumetric Shapes [Notes] CVPR 2015
Volumetric and Multi-View CNNs for Object Classification on 3D Data [Notes] CVPR 2016
Group Normalization [Notes] ECCV 2018
Spatial Transformer Networks [Notes] NIPS 2015
Frustum PointNets for 3D Object Detection from RGB-D Data (F-PointNet) [Notes] CVPR 2018
Dynamic Graph CNN for Learning on Point Clouds [Notes]
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (SOTA for 3D object detection) [Notes] CVPR 2019
MV3D: Multi-View 3D Object Detection Network for Autonomous Driving [Notes] CVPR 2017 (Baidu, sensor fusion, BV proposal)
AVOD: Joint 3D Proposal Generation and Object Detection from View Aggregation [Notes] IROS 2018 (sensor fusion, multiview proposal)
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [Notes]
Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gafp in 3D Object Detection for Autonomous Driving [Notes] CVPR 2019
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection CVPR 2018 (Apple, first end-to-end point cloud encoding to grid)
SECOND: Sparsely Embedded Convolutional Detection Sensors 2018 (builds on VoxelNet)
PointPillars: Fast Encoders for Object Detection from Point Clouds [Notes] CVPR 2019 (builds on SECOND)
Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite [Notes] CVPR 2012
Vision meets Robotics: The KITTI Dataset [Notes] IJRR 2013

2019-02 (9)

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset (I3D) [Notes]Video CVPR 2017
Initialization Strategies of Spatio-Temporal Convolutional Neural Networks [Notes] Video
Detect-and-Track: Efficient Pose Estimation in Videos [Notes] ICCV 2017 Video
Deep Learning Based Rib Centerline Extraction and Labeling [Notes] MI MICCAI 2018
SlowFast Networks for Video Recognition [Notes] ICCV 2019 Oral
Aggregated Residual Transformations for Deep Neural Networks (ResNeXt) [Notes] CVPR 2017
Beyond the pixel plane: sensing and learning in 3D (blog, 中文版本)
VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition (VoxNet) [Notes]
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation CVPR 2017 [Notes]
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space NIPS 2017 [Notes]
Review of Geometric deep learning 几何深度学习前沿 (from 知乎) (Up to CVPR 2018)

2019-01 (10)

DQN: Human-level control through deep reinforcement learning (Nature DQN paper) [Notes] DRL
Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection [Notes] MI
Panoptic Segmentation [Notes] PanSeg
Panoptic Feature Pyramid Networks [Notes] PanSeg
Attention-guided Unified Network for Panoptic Segmentation [Notes] PanSeg
Bag of Tricks for Image Classification with Convolutional Neural Networks [Notes] CLS
Deep Reinforcement Learning for Vessel Centerline Tracing in Multi-modality 3D Volumes [Notes] DRL MI
Deep Reinforcement Learning for Flappy Bird [Notes] DRL
Long-Term Feature Banks for Detailed Video Understanding [Notes] Video
Non-local Neural Networks [Notes] Video CVPR 2018

2018

Mask R-CNN
Cascade R-CNN: Delving into High Quality Object Detection
Focal Loss for Dense Object Detection (RetinaNet) [Notes]
Squeeze-and-Excitation Networks (SENet)
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Deformable Convolutional Networks ICCV 2017 [build on R-FCN]
Learning Region Features for Object Detection

2017 and before

Learning notes on Deep Learning
List of Papers on Machine Learning
Notes of Literature Review on CNN in CV This is the notes for all the papers in the recommended list here
Notes of Literature Review (Others)
Notes on how to set up DL/ML environment
Useful setup notes

Papers to Read

Here is the list of papers waiting to be read.

Deep Learning in general

Architecture Improvements

Reinforcement Learning

3D Perception

3D-CNN：3D Convolutional Neural Networks for Landing Zone Detection from LiDAR
Generative and Discriminative Voxel Modeling with Convolutional Neural Networks
Orientation-boosted Voxel Nets for 3D Object Recognition (ORION) <BMVC 2017>
GIFT: A Real-time and Scalable 3D Shape Search Engine CVPR 2016
3D Shape Segmentation with Projective Convolutional Networks (ShapePFCN)CVPR 2017
Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks
Open3D: A Modern Library for 3D Data Processing
Multimodal Deep Learning for Robust RGB-D Object Recognition IROS 2015
FlowNet3D: Learning Scene Flow in 3D Point Clouds CVPR 2019
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling CVPR 2018 (Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds)
PU-Net: Point Cloud Upsampling Network CVPR 2018
Recurrent Slice Networks for 3D Segmentation of Point Clouds CVPR 2018
SPLATNet: Sparse Lattice Networks for Point Cloud Processing CVPR 2018
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering NIPS 2016
Semi-Supervised Classification with Graph Convolutional Networks ICLR 2017
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks NIPS 2017
Graph Attention Networks ICLR 2018
3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection (3D SSD)
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models ICCV 2017
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis CVPR 2017
IPOD: Intensive Point-based Object Detector for Point Cloud
Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images CVPR 2017
2D-Driven 3D Object Detection in RGB-D Images
3D-SSD: Learning Hierarchical Features from RGB-D Images for Amodal 3D Object Detection
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection [classify occluded object]

Stereo and Flow

PSMNet: Pyramid Stereo Matching Network CVPR 2018
Stereo R-CNN based 3D Object Detection for Autonomous Driving CVPR 2019
Deep Rigid Instance Scene Flow CVPR 2019
Upgrading Optical Flow to 3D Scene Flow through Optical Expansion CVPR 2020
Learning Multi-Object Tracking and Segmentation from Automatic Annotations CVPR 2020 [automatic MOTS annotation]

Traffic light and traffic sign

Traffic-Sign Detection and Classification in the Wild CVPR 2016 [Tsinghua, Tencent, traffic signs]
A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection IEEE CRV 2018 [U torronto]
Detecting Traffic Lights by Single Shot Detection ITSC 2018
DeepTLR: A single Deep Convolutional Network for Detection and Classification of Traffic Lights IV 2016
Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data CVPR 2017 workshop
Traffic light recognition in varying illumination using deep learning and saliency map ITSC 2014 [traffic light]
Traffic light recognition using high-definition map features RAS 2019
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives TITS 2015

Datasets and Surveys

The DriveU Traffic Light Dataset: Introduction and Comparison with Existing Datasets ICRA 2018
The Oxford Radar RobotCar Dataset: A Radar Extension to the Oxford RobotCar Dataset
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives (traffic light survey, UCSD LISA)
Review of Graph Spectrum Theory (WIP)
3D Deep Learning Tutorial at CVPR 2017 [Notes] - (WIP)
A Survey on Neural Architecture Search
Network pruning tutorial (blog)
GNN tutorial at CVPR 2019
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset [Waymo, prediction dataset]
PANDA: A Gigapixel-level Human-centric Video Dataset CVPR 2020
WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving ICCV 2019 [Valeo]

Unsupervised depth estimation

Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation 3DV 2018
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network NIPS 2014 (Eigen et al)
Learning Depth from Monocular Videos using Direct Methods CVPR 2018 (monocular depth estimation)
Virtual-Normal: Enforcing geometric constraints of virtual normal for depth prediction [Notes] ICCV 2019 (better generation of PL)
Spatial Correspondence with Generative Adversarial Network: Learning Depth from Monocular Videos ICCV 2019
Unsupervised Collaborative Learning of Keyframe Detection and Visual Odometry Towards Monocular Deep SLAM ICCV 2019
Visualization of Convolutional Neural Networks for Monocular Depth Estimation ICCV 2019

Indoor Depth

Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation ICCV 2019 workshop [indoor]
Multi-Loss Rebalancing Algorithm for Monocular Depth Estimation ECCV 2020 [indoor depth]
Disambiguating Monocular Depth Estimation with a Single Transient ECCV 2020 [additional laser sensor, indoor depth]
Guiding Monocular Depth Estimation Using Depth-Attention Volume ECCV 2020 [indoor depth]
Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets ECCV 2020 [indoor depth]
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss ECCV 2020 [indoor depth]

lidar

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation (pointnet alternative, backbone)
Vehicle Detection from 3D Lidar Using Fully Convolutional Network (VeloFCN) RSS 2016
KPConv: Flexible and Deformable Convolution for Point Clouds (from the authors of PointNet)
PointCNN: Convolution On X-Transformed Points NIPS 2018
L3-Net: Towards Learning based LiDAR Localization for Autonomous Driving CVPR 2019
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement (sensor fusion, 3D mono proposal, refined in point cloud)
DeLS-3D: Deep Localization and Segmentation with a 3D Semantic Map CVPR 2018
Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection IROS 2019
PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing
Gated2Depth: Real-time Dense Lidar from Gated Images ICCV 2019 oral
A Multi-Sensor Fusion System for Moving Object Detection and Tracking in Urban Driving Environments ICRA 2014
PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation CVPR 2018 [sensor fusion, Zoox]
Deep Hough Voting for 3D Object Detection in Point Clouds ICCV 2019 [Charles Qi]
StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation CVPR 2020
Depth Sensing Beyond LiDAR Range CVPR 2020 [wide baseline stereo with trifocal]
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications IROS 2020 [lidar mapping]
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds CVPR 2020 oral [lidar segmentation]
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation CVPR 2020 [lidar segmentation]
OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression CVPR 2020 oral [lidar compression]
MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models NeurIPS 2020 oral [lidar compression]

Egocentric bbox prediction

Long-Term On-Board Prediction of People in Traffic Scenes under Uncertainty CVPR 2018 [on-board bbox prediction]
Unsupervised Traffic Accident Detection in First-Person Videos IROS 2019 (Honda)
NEMO: Future Object Localization Using Noisy Ego Priors (Honda)
Robust Aleatoric Modeling for Future Vehicle Localization (perspective)
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments WACV 2020 (perspective bbox, pedestrian)
Using panoramic videos for multi-person localization and tracking in a 3D panoramic coordinate

Lane Detection

End-to-end Lane Detection through Differentiable Least-Squares Fitting ICCV 2019
Line-CNN: End-to-End Traffic Line Detection With Line Proposal Unit TITS 2019 [object-like proposals]
Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers [3D LLD]
Ultra Fast Structure-aware Deep Lane Detection ECCV 2020 [lane detection]
A Novel Approach for Detecting Road Based on Two-Stream Fusion Fully Convolutional Network (convert camera to BEV)
FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network

Tracking

RetinaTrack: Online Single Stage Joint Detection and Tracking CVPR 2020
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art (latest update in Dec 2019)
Simultaneous Identification and Tracking of Multiple People Using Video and IMUs CVPR 2019
Detect-and-Track: Efficient Pose Estimation in Videos
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
Video Action Transformer Network CVPR 2019 oral
Online Real-time Multiple Spatiotemporal Action Localisation and Prediction ICCV 2017
多目标跟踪近年论文及开源代码汇总
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning CVPR 2020 oral [3DMOT, CMU, Kris Kitani]
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking ECCV 2020 spotlight [MOT, Tencent]
Towards Real-Time Multi-Object Tracking ECCV 2020 [MOT]
Probabilistic 3D Multi-Object Tracking for Autonomous Driving [TRI]

keypoints: pose and face

Probabilistic Face Embeddings ICCV 2019
Data Uncertainty Learning in Face Recognition CVPR 2020
Self-Supervised Learning of Interpretable Keypoints From Unlabelled Videos CVPR 2020 oral [VGG, self-supervised, interpretable, discriminator]

General DL

Revisiting Small Batch Training for Deep Neural Networks
ICML2019 workshop: Adaptive and Multitask Learning: Algorithms & Systems ICML 2019
Adaptive Scheduling for Multi-Task Learning NIPS 2018 (NMT)
Polar Transformer Networks ICLR 2018
Measuring Calibration in Deep Learning CVPR 2019
Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation ICCV 2019 (epistemic uncertainty)
Making Convolutional Networks Shift-Invariant Again ICML
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty NeurIPS 2019
Understanding deep learning requires rethinking generalization ICLR 2017 [ICLR best paper]
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks ICLR 2017 (NLL score as anomaly score)
Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination CVPR 2018 spotlight (Stella Yu)
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks TIP 2018
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning ICML 2018
Designing Network Design Spaces CVPR 2020
Moco2: Improved Baselines with Momentum Contrastive Learning
SGD on Neural Networks Learns Functions of Increasing Complexity NIPS 2019 (SGD learns a linear classifier first)
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images BMVC 2018 (multi-bin, what's new?)
In-Place Activated BatchNorm for Memory-Optimized Training of DNNs CVPR 2018 (optimized BatchNorm + ReLU)
FCNN: Fourier Convolutional Neural Networks (FFT as CNN)
Visualizing the Loss Landscape of Neural Nets NIPS 2018
Xception: Deep Learning with Depthwise Separable Convolutions (Xception)
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (uncertainty)
Learning to Drive from Simulation without Real World Labels ICRA 2019 (domain adaptation, sim2real)
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks CVPR 2020 oral
Switchable Whitening for Deep Representation Learning ICCV 2019 [domain adaptation]
Visual Chirality CVPR 2020 oral [best paper nominee]
Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data CVPR 2020
Self-training with Noisy Student improves ImageNet classification CVPR 2020 [distillation]
Keep it Simple: Image Statistics Matching for Domain Adaptation CVPRW 2020 [Domain adaptation for 2D mod bbox]
Epipolar Transformers CVPR 2020 [Yihui He]
Scalable Uncertainty for Computer Vision With Functional Variational Inference CVPR 2020 [epistemic uncertainty with one fwd pass]

Mono3D

3DOP: 3D Object Proposals for Accurate Object Class Detection NIPS 2015
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery ECCV 2018 (Monocular 3D object detection and depth estimation)
Towards Scene Understanding: Unsupervised Monocular Depth Estimation with Semantic-aware Representation CVPR 2019 [unified conditional decoder]
DDP: Dense Depth Posterior from Single Image and Sparse Range CVPR 2019
Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes IJCV 2018 (data augmentation with AR, Toyota)
Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data IITS
Towards Scene Understanding with Detailed 3D Object Representations IJCV 2014 (keypoint, 3D bbox annotation)
Deep Cuboid Detection: Beyond 2D Bounding Boxes (Magic Leap)
Viewpoints and Keypoints (Malik)
Lifting Object Detection Datasets into 3D (PASCAL)
3D Object Class Detection in the Wild (keypoint based)
Fast Single Shot Detection and Pose Estimation 3DV 2016 (SSD + pose, Wei Liu)
Virtual KITTI 2
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing CVPR 2017
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views ICCV 2015 Oral
Real-Time Seamless Single Shot 6D Object Pose Prediction CVPR 2018
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching NIPS 2018 [disparity estimation]
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera ICRA 2019
Learning Depth with Convolutional Spatial Propagation Network (Baidu, depth from SPN) ECCV 2018
Just Go with the Flow: Self-Supervised Scene Flow Estimation CVPR 2020 oral [Scene flow, Lidar]
Online Depth Learning against Forgetting in Monocular Videos CVPR 2020 [monodepth]
Self-Supervised Deep Visual Odometry with Online Adaptation CVPR 2020 oral [DF-VO, TrianFlow, meta-learning]
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity Volume CVPR 2020
Online Depth Learning against Forgetting in Monocular Videos CVPR 2020 [monodepth, online learning]
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation CVPR 2020 [monodepth, semantic]
Inferring Distributions Over Depth from a Single Image TRO [Depth confidence, stitching them together]
Novel View Synthesis of Dynamic Scenes with Globally Coherent Depths CVPR 2020
The Edge of Depth: Explicit Constraints between Segmentation and Depth CVPR 2020 [Xiaoming Liu, multimodal, depth bleeding]

Radar Perception

MV-RSS: Multi-View Radar Semantic Segmentation ICCV 2021
Classification of Objects in Polarimetric Radar Images Using CNNs at 77 GHz (Radar, polar)
CNNs for Interference Mitigation and Denoising in Automotive Radar Using Real-World Data NeurIPS 2019 (radar)
Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation ICCV 2019 (radar)
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects ECCV 2020 [Uber ATG]
Depth Estimation from Monocular Images and Sparse Radar Data IROS 2020 [Camera + Radar for monodepth, nuscenes]
RPR: Radar-Camera Sensor Fusion for Joint Object Detection and Distance Estimation in Autonomous Vehicles IROS 2020 [radar proposal refinement]
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications

SLAM

PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization [Notes] ICCV 2015
PoseNet2: Modelling Uncertainty in Deep Learning for Camera Relocalization ICRA 2016
PoseNet3: Geometric Loss Functions for Camera Pose Regression with Deep Learning CVPR 2017
EssNet: Convolutional neural network architecture for geometric matching CVPR 2017
NC-EssNet: Neighbourhood Consensus Networks NeurIPS 2018
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task CVPR 2020 oral [Eric Brachmann, ngransac]
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints CVPR 2018
[DynSLAM: Robust Dense Mapping for Large-Scale Dynamic Environments](https://siegedog.com/dynsla

Truncated — view the full README on GitHub.

3d-object-detection

3d-object-recognition

cnn

computer-vision

deep-learning

literature-review

machine-learning

medical

medical-imaging

paper

paper-reading

paper-review

point-cloud

reinforcement-learning

Contributors

patrick-llgc

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YuShen1116

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About me

My name is Patrick Langechuan Liu. After about a decade of training and research in physics, I found my passion in deep learning and autonomous driving.

I am currently Director of AI at Nvidia, leading the ML modeling effort for Nvidia's end-to-end autonomous driving project, Alpamayo.