lxtGH/Awesome-Segmentation-With-Transformer

[T-PAMI-2024] Transformer-Based Visual Segmentation: A Survey

755

19 commits

updated Aug 25, 2024

See the code

README

Awesome PR's Welcome

Transformer-Based Visual Segmentation: A Survey

T-PAMI, 2024
Xiangtai Li (Project Lead) · Henghui Ding · Haobo Yuan · Wenwei Zhang · Guangliang Cheng
Jiangmiao Pang . Kai Chen . Ziwei Liu . Chen Change Loy

arXiv PDF S-Lab Project Page TPAMI PDF


This repo is used for recording, tracking, and benchmarking several recent transformer-based visual segmentation methods, as a supplement to our survey.
If you find any work missing or have any suggestions (papers, implementations and other resources), feel free to pull requests. We will add the missing papers to this repo ASAP.

🔥News

[-] Accepted by T-PAMI-2024.

[-] Add several CVPR-24 works on this directions. 2024-03. You are welcome to add your CVPR works in our repos!

[-] The third version is on arxiv. survey More benchmark and methods are included!!. 2023-12.

[-] The second draft is on arxiv. 2023-06.

🔥Highlight!!

[1], Previous transformer surveys divide the methods by the different tasks and settings. Different from them, we re-visit and group the existing transformer-based methods from the technical perspective.

[2], We survey the methods in two parts: one for the mainstream tasks based on DETR-like meta-architecture, the other for related directions according to the tasks.

[3], We further re-benchmark several representative works on image semantic segmentation and panoptic segmentation datasets.

[4], We also include the query-based detection transformers since both segmentation and detection tasks are unified by object query.

Introduction

In this survey, we present the first detailed survey on Transformer-Based Segmentation.

Alt Text

Summary of Contents

Methods: A Survey

Meta-Architecture

Strong Representation

Better ViTs Design

Hybrid CNNs/Transformers/MLPs

Self-Supervised Learning

Interaction Design in Decoder

Improved Cross Attention Design

Spatial-Temporal Cross Attention Design

Optimizing Object Query

Adding Position Information into Query

Adding Extra Supervision into Query

Using Query For Association

Query as Instance Association

Query as Linking Multi-Tasks

Conditional Query Generation

Conditional Query Fusion on Language Features

Conditional Query Fusion on Cross Image Features

Tuning Foundation Models

Vision Adapter

Open Vocabulary Learning

Point Cloud Segmentation

Domain-aware Segmentation

Label and Model Efficient Segmentation

Class Agnostic Segmentation and Tracking

Medical Image Segmentation

Acknowledgement

If you find our survey and repository useful for your research project, please consider citing our paper:

@article{li2023transformer,
    author={Li, Xiangtai and Ding, Henghui and Zhang, Wenwei and Yuan, Haobo and Cheng, Guangliang and Jiangmiao, Pang and Chen, Kai and Liu, Ziwei and Loy, Chen Change},
    title={Transformer-Based Visual Segmentation: A Survey},
    journal={T-PAMI},
    year={2024}
  }

Contact

xiangtai94@gmail.com (main)
lxtpku@pku.edu.cn

Attention Model Repo by Min-Hung (Steve) Chen.

Detection Transformer Repo by IDEA.

Open Vocabulary Learning Repo by PKU and NTU.

Contributors

lxtGH

16 commits

HarborYuan

2 commits

lxtGH/Awesome-Segmentation-With-Transformer

[T-PAMI-2024] Transformer-Based Visual Segmentation: A Survey

755

19 commits

updated Aug 25, 2024

See the code

README

Awesome PR's Welcome

Transformer-Based Visual Segmentation: A Survey

T-PAMI, 2024
Xiangtai Li (Project Lead) · Henghui Ding · Haobo Yuan · Wenwei Zhang · Guangliang Cheng
Jiangmiao Pang . Kai Chen . Ziwei Liu . Chen Change Loy

arXiv PDF S-Lab Project Page TPAMI PDF


This repo is used for recording, tracking, and benchmarking several recent transformer-based visual segmentation methods, as a supplement to our survey.
If you find any work missing or have any suggestions (papers, implementations and other resources), feel free to pull requests. We will add the missing papers to this repo ASAP.

🔥News

[-] Accepted by T-PAMI-2024.

[-] Add several CVPR-24 works on this directions. 2024-03. You are welcome to add your CVPR works in our repos!

[-] The third version is on arxiv. survey More benchmark and methods are included!!. 2023-12.

[-] The second draft is on arxiv. 2023-06.

🔥Highlight!!

[1], Previous transformer surveys divide the methods by the different tasks and settings. Different from them, we re-visit and group the existing transformer-based methods from the technical perspective.

[2], We survey the methods in two parts: one for the mainstream tasks based on DETR-like meta-architecture, the other for related directions according to the tasks.

[3], We further re-benchmark several representative works on image semantic segmentation and panoptic segmentation datasets.

[4], We also include the query-based detection transformers since both segmentation and detection tasks are unified by object query.

Introduction

In this survey, we present the first detailed survey on Transformer-Based Segmentation.

Alt Text

Summary of Contents

Methods: A Survey

Meta-Architecture

Strong Representation

Better ViTs Design

Hybrid CNNs/Transformers/MLPs

Self-Supervised Learning

Interaction Design in Decoder

Improved Cross Attention Design

Spatial-Temporal Cross Attention Design

Optimizing Object Query

Adding Position Information into Query

Adding Extra Supervision into Query

Using Query For Association

Query as Instance Association

Query as Linking Multi-Tasks

Conditional Query Generation

Conditional Query Fusion on Language Features

Conditional Query Fusion on Cross Image Features

Tuning Foundation Models

Vision Adapter

Open Vocabulary Learning

Point Cloud Segmentation

Domain-aware Segmentation

Label and Model Efficient Segmentation

Class Agnostic Segmentation and Tracking

Medical Image Segmentation

Acknowledgement

If you find our survey and repository useful for your research project, please consider citing our paper:

@article{li2023transformer,
    author={Li, Xiangtai and Ding, Henghui and Zhang, Wenwei and Yuan, Haobo and Cheng, Guangliang and Jiangmiao, Pang and Chen, Kai and Liu, Ziwei and Loy, Chen Change},
    title={Transformer-Based Visual Segmentation: A Survey},
    journal={T-PAMI},
    year={2024}
  }

Contact

xiangtai94@gmail.com (main)
lxtpku@pku.edu.cn

Attention Model Repo by Min-Hung (Steve) Chen.

Detection Transformer Repo by IDEA.

Open Vocabulary Learning Repo by PKU and NTU.

Contributors

lxtGH

16 commits

HarborYuan

2 commits