Yunlong Tang1,*, Jing Bi1,*, Siting Xu2,*, Luchuan Song1, Susan Liang1 , Teng Wang2,3 , Daoan Zhang1 , Jie An1 , Jingyang Lin1 , Rongyi Zhu1 , Ali Vosoughi1 , Chao Huang1 , Zeliang Zhang1 , Pinxin Liu1 , Mingqian Feng1 , Feng Zheng2 , Jianguo Zhang2 , Ping Luo3 , Jiebo Luo1, Chenliang Xu1,†. (*Core Contributors, †Corresponding Authors)
1University of Rochester, 2Southern University of Science and Technology, 3The University of Hong Kong
[07/23/2024]
📢 We've recently updated our survey: “Video Understanding with Large Language Models: A Survey”!
✨ This comprehensive survey covers video understanding techniques powered by large language models (Vid-LLMs), training strategies, relevant tasks, datasets, benchmarks, and evaluation methods, and discusses the applications of Vid-LLMs across various domains.
🚀 What's New in This Update:
✅ Updated to include around 100 additional Vid-LLMs and 15 new benchmarks as of June 2024.
✅ Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM functionality.
✅ Added a Preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section.
✅ Added a new Training Strategies chapter, removing adapters as a factor for model classification.
✅ All figures and tables have been redesigned.
Multiple minor updates will follow this major update. And the GitHub repository will be gradually updated soon. We welcome your reading and feedback ❤️
Table of Contents- Awesome-LLMs-for-Video-Understanding
If you find our survey useful for your research, please cite the following paper:
@article{vidllmsurvey,
title={Video Understanding with Large Language Models: A Survey},
author={Tang, Yunlong and Bi, Jing and Xu, Siting and Song, Luchuan and Liang, Susan and Wang, Teng and Zhang, Daoan and An, Jie and Lin, Jingyang and Zhu, Rongyi and Vosoughi, Ali and Huang, Chao and Zhang, Zeliang and Zheng, Feng and Zhang, Jianguo and Luo, Ping and Luo, Jiebo and Xu, Chenliang},
journal={arXiv preprint arXiv:2312.17432},
year={2023},
}| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
| Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language | Socratic Models | 04/2022 | project page | arXiv |
Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions |
Video ChatCaptioner | 04/2023 | code | arXiv |
VLog: Video as a Long Document |
VLog | 04/2023 | code | - |
| ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System | ChatVideo | 04/2023 | project page | arXiv |
| MM-VID: Advancing Video Understanding with GPT-4V(ision) | MM-VID | 10/2023 | - | arXiv |
MISAR: A Multimodal Instructional System with Augmented Reality |
MISAR | 10/2023 | project page | ICCV |
| Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos | Grounding-Prompter | 12/2023 | - | arXiv |
| NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation | NaVid | 02/2024 | project page - | RSS |
| VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding | VideoAgent | 03/2024 | project page | arXiv |
| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
Learning Video Representations from Large Language Models |
LaViLa | 12/2022 | code | CVPR |
| Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | Vid2Seq | 02/2023 | code | CVPR |
VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset |
VAST | 05/2023 | code | NeurIPS |
| Merlin:Empowering Multimodal LLMs with Foresight Minds | Merlin | 12/2023 | - | arXiv |
| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
Otter: A Multi-Modal Model with In-Context Instruction Tuning |
Otter | 06/2023 | code | arXiv |
VideoLLM: Modeling Video Sequence with Large Language Models |
VideoLLM | 05/2023 | code | arXiv |
| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
VTimeLLM: Empower LLM to Grasp Video Moments |
VTimeLLM | 11/2023 | code | arXiv |
| GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation | GPT4Video | 11/2023 | - | arXiv |
| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
VideoChat: Chat-Centric Video Understanding |
VideoChat | 05/2023 | code demo | arXiv |
PG-Video-LLaVA: Pixel Grounding Large Video-Language Models |
PG-Video-LLaVA | 11/2023 | code | arXiv |
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding |
TimeChat | 12/2023 | code | CVPR |
Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding |
Video-GroundingDINO | 12/2023 | code | arXiv |
| A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot | Video4096 | 05/2023 | EMNLP |
| Title | Model | Date | Code | Venue |
|---|---|---|---|---|
| SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models | SlowFast-LLaVA | 07/2024 | - | arXiv |
| Name | Paper | Date | Link | Venue |
|---|---|---|---|---|
| Charades | Hollywood in homes: Crowdsourcing data collection for activity understanding | 2016 | Link | ECCV |
| YouTube8M | YouTube-8M: A Large-Scale Video Classification Benchmark | 2016 | Link | - |
| ActivityNet | ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding | 2015 | Link | CVPR |
| Kinetics-GEBC | GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval | 2022 | Link | ECCV |
| Kinetics-400 | The Kinetics Human Action Video Dataset | 2017 | Link | - |
| VidChapters-7M | VidChapters-7M: Video Chapters at Scale | 2023 | Link | NeurIPS |
| Name | Paper | Date | Link | Venue |
|---|---|---|---|---|
| Epic-Kitchens-100 | Rescaling Egocentric Vision | 2021 | Link | IJCV |
| VCR (Visual Commonsense Reasoning) | From Recognition to Cognition: Visual Commonsense Reasoning | 2019 | Link | CVPR |
| Ego4D-MQ and Ego4D-NLQ | Ego4D: Around the World in 3,000 Hours of Egocentric Video | 2021 | Link | CVPR |
| Vid-STG | Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences | 2020 | Link | CVPR |
| Charades-STA | TALL: Temporal Activity Localization via Language Query | 2017 | Link | ICCV |
| DiDeMo | Localizing Moments in Video with Natural Language | 2017 | Link | ICCV |
| Name | Paper | Date | Link | Venue |
|---|---|---|---|---|
| MSVD-QA | Video Question Answering via Gradually Refined Attention over Appearance and Motion | 2017 | Link | ACM Multimedia |
| MSRVTT-QA | Video Question Answering via Gradually Refined Attention over Appearance and Motion | 2017 | Link | ACM Multimedia |
| TGIF-QA | TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering | 2017 | Link | CVPR |
| ActivityNet-QA | ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering | 2019 | Link | AAAI |
| Pororo-QA | DeepStory: Video Story QA by Deep Embedded Memory Networks | 2017 | Link | IJCAI |
| TVQA | TVQA: Localized, Compositional Video Question Answering | 2018 | Link | EMNLP |
| Name | Paper | Date | Link | Venue |
|---|---|---|---|---|
| VidChapters-7M | VidChapters-7M: Video Chapters at Scale | 2023 | Link | NeurIPS |
| VALOR-1M | VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset | 2023 | Link | arXiv |
| Youku-mPLUG | Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks | 2023 | Link | arXiv |
| InternVid | InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation | 2023 | Link | arXiv |
| VAST-27M | VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset | 2023 | Link | NeurIPS |
| Name | Paper | Date | Link | Venue |
|---|---|---|---|---|
| MIMIC-IT | MIMIC-IT: Multi-Modal In-Context Instruction Tuning | 2023 | Link | arXiv |
| VideoInstruct100K | Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models | 2023 | Link | arXiv |
| TimeIT | TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding | 2023 | Link | CVPR |
We welcome everyone to contribute to this repository and help improve it. You can submit pull requests to add new papers, projects, and helpful materials, or to correct any errors that you may find. Please make sure that your pull requests follow the "Title|Model|Date|Code|Venue" format. Thank you for your valuable contributions!