Awesome Multimodal Named Entity Recognition 🎶📜

Sorry, this repo will be not updated, since I already changed my research topic.

Content

• • 1. Description
• • 2. Topic Order
  • • 👑 Dataset
• • 3. Chronological Order
  • • Survey
  • • 2020
  • • 2021
  • • 2022
  • • 2023
• • 4. Courses
• • 5. Thinking MNER

• Contact Me

1.Description

🐌 Markdown Format:

• (Conference/Journal Year) Title, First Author et al. [Paper] [Code] [Project]
  
• (Conference/Journal Year) [💬Topic] Title, First Author et al. [Paper] [Code] [Project]
  • (Optional) 🌱 or 📌
  • (Optional) 🚀 or 👑 or 📚

• 🌱: Novel idea
• 📌: The first...
• 🚀: State-of-the-Art
• 👑: Novel dataset/model
• 📚：Downstream Tasks

2. Topic Order

• 👑 Dataset
  • (AAAI 2017) Adaptive Co-attention Network for Named Entity Recognition in Tweets [paper]
  • (ACL 2018) Visual Attention Model for Name Tagging in Multimodal Social Media [paper]

3. Chronological Order

• 2020

  • (ACL 2020) Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer [paper]
  • (COLING 2020) RIVA: A Pre-trained Tweet Multimodal Model Based on Text-image Relation for Multimodal NER [paper]

• 2021

  • (AAAI 2021) Multi-modal Graph Fusion for Named Entity Recognition with Targeted Visual Guidance [paper]
  • (AAAI 2021) RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER [paper] [code]
  • (EMNLP 2021) Can images help recognize entities? A study of the role of images for Multimodal NER [paper] [code]

• 2022

  • (COLING 2022) Flat Multi-modal Interaction Transformer for Named Entity Recognition [paper]

    • 📌 1st interpolating FLAT with MNER
    • 🚀 SOTA on Twitter15 with Bert_base_uncased but code is unavailable

  • (NAACL Findings 2022) Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction [paper] [code]

    • 📌 code using refined Twitter15 dataset

  • (WSDM 2022) MAF: A General Matching and Alignment Framework for Multimodal Named Entity Recognition [paper] [code]

  • (SIGIR 2022) Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion [paper] [paper]

    • 📌 1st fully Transformer structure
    • 🚀 SOTA on Twitter17 using Bert_base_uncased but only implement on Twitter17

  • (NAACL 2022) ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition [paper] [code]

    • 📌 Roberta_large as backbone provides powerful improvements
    • 🌱 Using OCR ect without directly using images

  • (MM 2022) Query Prior Matters: A MRC Framework for Multimodal Named Entity Recognition [paper]

    • 🌱 1st MRC based framework for MNER

  • (SIGIR 2022) Learning from Different text-image Pairs: A Relation-enhanced Graph Convolutional Network for Multimodal NER [paper]

    • 📌 Trustworthy performance by reimplementation

  • (ICME 2022) CAT-MNER: Multimodal Named Entity Recognition with Knowledge-Refined Cross-Modal Attention [paper]

    • 🚀 SOTA on Twitter15 and Twitter17 with Roberta_large
    • 📌 Require 8 V100 GPU

  • (DSAA 2022) PromptMNER: Prompt-Based Entity-Related Visual Clue Extraction and Integration for Multimodal Named Entity Recognition [paper]

    • 🚀 SOTA on Twitter15 and Twitter17 with Roberta_large
    • 📌 Require 8 V100 GPU
    • 🌱 Prompt-based

  • （arxiv 2022） Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media [paper] [code]

  • (arxiv 2021) Multi-Granularity Contrastive Knowledge Distillation for Multimodal Named Entity Recognition

    • submitted to ACL2021 but not accepted

  • (arxiv 2022) MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding [paper]

• 2023

  • (EMNLP 2023) Named Entity and Relation Extraction with Multi-Modal Retrieval [paper] [code in construction]
    • 🚀 retrieve augmentation in MNER
  • (MM 2023) Learning Implicit Entity-object Relations by Bidirectional Generative Alignment for Multimodal NER [paper]
    • 🚀 generative alignment
    • 🌱 free from image during inference

4. Course

5. Thinking MNER

• MNER is a hard task since it needs multimodal understanding in social media domain. However, existing methods simplify it to extacting helpful viusal clue to assist NER, with a simple showcase. In twitter datasets, the image-text pair always has no or vague relationship, which needs extra information or supervision for model to understand. Therefore, I believe that is why MNER-QG, MoRe, R-GCN and PromptMNER work. However, existing works are still nowhere near logical understanding, since they all introduce out-sample knowledge. Now I am trying to introduce knowledge graph in MNER to provide in-sample context.

• Tricky task: When I developed my work (SOTA in two datasets but in submission), I found

  1 only tuning task head of BERT (freeze Bert and ViT) can achieve comparable results (0.2-0.5% drop). So I believe we can directly introduce prompt eniggering for text.

  2 large language model matters more than fancy innovation

  3 using a empty image to replace all valid images during test only drop 2%

  4 simple loss like contractive loss brings 2% improvement. So I think the model heavily focuses on text.

  5 Applying the same code in different environments even in different GPUs has results with large variance...

  6 It has two mainstreams depending on image+text or caption+text as input. DAMO-series works mainly focus on the latter one, which is proved to be more extandable and SOTA than others.

Contact Me

• Feel free to contact me by email (chenfeng1271@gmail.com)