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https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding
🔥🔥🔥Latest Papers, Codes and Datasets on Vid-LLMs.
https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding
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🔥🔥🔥Latest Papers, Codes and Datasets on Vid-LLMs.
- Host: GitHub
- URL: https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding
- Owner: yunlong10
- Created: 2023-07-16T07:00:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-29T14:53:22.000Z (5 months ago)
- Last Synced: 2024-05-23T01:06:04.228Z (4 months ago)
- Homepage:
- Size: 1.06 MB
- Stars: 739
- Watchers: 23
- Forks: 41
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-VideoLLM-Papers - Awesome-LLMs-for-Video-Understanding
- Awesome-Video-Robotic-Papers - Awesome-LLMs-for-Video-Understanding
README
# Awesome-LLMs-for-Video-Understanding [](https://awesome.re)
### 🔥🔥🔥 [Video Understanding with Large Language Models: A Survey](https://arxiv.org/pdf/2312.17432v2.pdf)
> *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 , 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*
**[Paper](https://arxiv.org/pdf/2312.17432v2.pdf)** | **[Project Page](https://github.com/yunlong10/Awesome-LLMs-for-Video-Understanding)**
Table of Contents
- [Awesome-LLMs-for-Video-Understanding ](#awesome-llms-for-video-understanding-)
- [🔥🔥🔥 Video Understanding with Large Language Models: A Survey](#-video-understanding-with-large-language-models-a-survey)
- [Why we need Vid-LLMs?](#why-we-need-vid-llms)
- [😎 Vid-LLMs: Models](#-vid-llms-models)
- [🤖 LLM-based Video Agents](#-llm-based-video-agents)
- [👾 Vid-LLM Pretraining](#-vid-llm-pretraining)
- [👀 Vid-LLM Instruction Tuning](#-vid-llm-instruction-tuning)
- [Fine-tuning with Connective Adapters](#fine-tuning-with-connective-adapters)
- [Fine-tuning with Insertive Adapters](#fine-tuning-with-insertive-adapters)
- [Fine-tuning with Hybrid Adapters](#fine-tuning-with-hybrid-adapters)
- [🦾 Hybrid Methods](#-hybrid-methods)
- [Tasks, Datasets, and Benchmarks](#tasks-datasets-and-benchmarks)
- [Recognition and Anticipation](#recognition-and-anticipation)
- [Captioning and Description](#captioning-and-description)
- [Grounding and Retrieval](#grounding-and-retrieval)
- [Question Answering](#question-answering)
- [Video Instruction Tuning](#video-instruction-tuning)
- [Pretraining Dataset](#pretraining-dataset)
- [Fine-tuning Dataset](#fine-tuning-dataset)
- [Video-based Large Language Models Benchmark](#video-based-large-language-models-benchmark)
- [Contributing](#contributing)
- [📑 Citation](#-citation)
- [🌟 Star History](#-star-history)
- [♥️ Contributors](#️-contributors)
## Why we need Vid-LLMs?
## 😎 Vid-LLMs: Models
### 🤖 LLM-based Video Agents
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :-----------------: | :-----: | :----------------------------------------------------------: | :---: |
| [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language**](https://arxiv.org/abs/2204.00598) | Socratic Models | 04/2022 | [project page](https://socraticmodels.github.io/) | arXiv |
| [**Video ChatCaptioner: Towards Enriched Spatiotemporal Descriptions**](https://arxiv.org/abs/2304.04227)[](https://github.com/Vision-CAIR/ChatCaptioner/tree/main/Video_ChatCaptioner) | Video ChatCaptioner | 04/2023 | [code](https://github.com/Vision-CAIR/ChatCaptioner/tree/main/Video_ChatCaptioner) | arXiv |
| [**VLog: Video as a Long Document**](https://github.com/showlab/VLog)[](https://github.com/showlab/VLog) | VLog | 04/2023 | [code](https://huggingface.co/spaces/TencentARC/VLog) | - |
| [**ChatVideo: A Tracklet-centric Multimodal and Versatile Video Understanding System**](https://arxiv.org/abs/2304.14407) | ChatVideo | 04/2023 | [project page](https://www.wangjunke.info/ChatVideo/) | arXiv |
| [**MM-VID: Advancing Video Understanding with GPT-4V(ision)**](https://arxiv.org/abs/2310.19773) | MM-VID | 10/2023 | - | arXiv |
| [**MISAR: A Multimodal Instructional System with Augmented Reality**](https://arxiv.org/abs/2310.11699v1)[](https://github.com/nguyennm1024/misar) | MISAR | 10/2023 | [project page](https://github.com/nguyennm1024/misar) | ICCV |
| [**Grounding-Prompter: Prompting LLM with Multimodal Information for Temporal Sentence Grounding in Long Videos**](https://arxiv.org/abs/2312.17117) | Grounding-Prompter | 12/2023 | - | arXiv |
| [**NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation**](https://arxiv.org/pdf/2402.15852) | NaVid | 02/2024 | [project page](https://pku-epic.github.io/NaVid/) - | RSS |
| [**VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding**](https://arxiv.org/abs/2403.11481) | VideoAgent | 03/2024 | [project page](https://videoagent.github.io/) | arXiv |
### 👾 Vid-LLM Pretraining
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :-----: | :-----: | :------------------------------------------------: | :-----: |
| [**Learning Video Representations from Large Language Models**](https://arxiv.org/abs/2212.04501)[](https://github.com/facebookresearch/lavila) | LaViLa | 12/2022 | [code](https://github.com/facebookresearch/lavila) | CVPR |
| [**Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning**](https://arxiv.org/abs/2302.14115) | Vid2Seq | 02/2023 | [code](https://antoyang.github.io/vid2seq.html) | CVPR |
| [**VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset**](https://arxiv.org/abs/2305.18500v1)[](https://github.com/txh-mercury/vast) | VAST | 05/2023 | [code](https://github.com/txh-mercury/vast) | NeurIPS |
| [**Merlin:Empowering Multimodal LLMs with Foresight Minds**](https://arxiv.org/abs/2312.00589v1) | Merlin | 12/2023 | - | arXiv |
### 👀 Vid-LLM Instruction Tuning
#### Fine-tuning with Connective Adapters
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :-----------: | :-----: | :--------------------------------------------------: | :---: |
| [**Video-LLaMA: An Instruction-Finetuned Visual Language Model for Video Understanding**](https://arxiv.org/abs/2306.02858) [](https://github.com/DAMO-NLP-SG/Video-LLaMA) | Video-LLaMA | 06/2023 | [code](https://github.com/DAMO-NLP-SG/Video-LLaMA) | arXiv |
| [**VALLEY: Video Assistant with Large Language model Enhanced abilitY**](https://arxiv.org/abs/2306.07207)[](https://github.com/RupertLuo/Valley) | VALLEY | 06/2023 | [code](https://github.com/RupertLuo/Valley) | - |
| [**Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models**](https://arxiv.org/abs/2306.05424)[](https://github.com/mbzuai-oryx/Video-ChatGPT) | Video-ChatGPT | 06/2023 | [code](https://github.com/mbzuai-oryx/Video-ChatGPT) | arXiv |
| [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration**](https://arxiv.org/abs/2306.09093)[](https://github.com/lyuchenyang/macaw-llm) | Macaw-LLM | 06/2023 | [code](https://github.com/lyuchenyang/macaw-llm) | arXiv |
| [**LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning**](https://arxiv.org/abs/2306.10354) [](https://github.com/zjr2000/llmva-gebc) | LLMVA-GEBC | 06/2023 | [code](https://github.com/zjr2000/llmva-gebc) | CVPR |
| [**Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks**](https://arxiv.org/abs/2306.04362) [](https://github.com/x-plug/youku-mplug) | mPLUG-video | 06/2023 | [code](https://github.com/x-plug/youku-mplug) | arXiv |
| [**MovieChat: From Dense Token to Sparse Memory for Long Video Understanding**](https://arxiv.org/abs/2307.16449)[](https://github.com/rese1f/MovieChat) | MovieChat | 07/2023 | [code](https://github.com/rese1f/MovieChat) | arXiv |
| [**Large Language Models are Temporal and Causal Reasoners for Video Question Answering**](https://arxiv.org/abs/2310.15747)[](https://github.com/mlvlab/Flipped-VQA) | LLaMA-VQA | 10/2023 | [code](https://github.com/mlvlab/Flipped-VQA) | EMNLP |
| [**Video-LLaVA: Learning United Visual Representation by Alignment Before Projection**](https://arxiv.org/pdf/2311.10122v2.pdf)[](https://github.com/PKU-YuanGroup/Video-LLaVA) | Video-LLaVA | 11/2023 | [code](https://github.com/PKU-YuanGroup/Video-LLaVA) | arXiv |
| [**Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding**](https://arxiv.org/pdf/2311.17043v1.pdf)[](https://github.com/pku-yuangroup/chat-univi) | Chat-UniVi | 11/2023 | [code](https://github.com/pku-yuangroup/chat-univi) | arXiv |
| [**LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models**](https://arxiv.org/abs/2311.08046v1)[](https://github.com/dvlab-research/LLaMA-VID) | LLaMA-VID | 11/2023 | [code](https://github.com/dvlab-research/LLaMA-VID) | arXiv |
| [**VISTA-LLAMA: Reliable Video Narrator via Equal Distance to Visual Tokens**](https://arxiv.org/abs/2312.08870) | VISTA-LLAMA | 12/2023 | - | arXiv |
| [**Audio-Visual LLM for Video Understanding**](https://arxiv.org/abs/2312.06720) | - | 12/2023 | - | arXiv |
| [**AutoAD: Movie Description in Context**](https://openaccess.thecvf.com/content/CVPR2023/papers/Han_AutoAD_Movie_Description_in_Context_CVPR_2023_paper.pdf) | AutoAD | 06/2023 | [code](https://github.com/TengdaHan/AutoAD) | CVPR |
| [**AutoAD II: The Sequel - Who, When, and What in Movie Audio Description**](https://openaccess.thecvf.com//content/ICCV2023/papers/Han_AutoAD_II_The_Sequel_-_Who_When_and_What_in_ICCV_2023_paper.pdf) | AutoAD II | 10/2023 | - | ICCV |
| [**Fine-grained Audio-Visual Joint Representations for Multimodal Large Language Models**](https://arxiv.org/abs/2310.05863v2)[](https://github.com/the-anonymous-bs/favor) | FAVOR | 10/2023 | [code](https://github.com/the-anonymous-bs/favor) | arXiv |
| [**VideoLLaMA2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs**](https://arxiv.org/abs/2406.07476)[](https://github.com/DAMO-NLP-SG/VideoLLaMA2) | VideoLLaMA2 | 06/2024 | [code](https://github.com/DAMO-NLP-SG/VideoLLaMA2) | arXiv |
#### Fine-tuning with Insertive Adapters
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :------: | :-----: | :----------------------------------------: | :---: |
| [**Otter: A Multi-Modal Model with In-Context Instruction Tuning**](https://arxiv.org/abs/2305.03726v1)[](https://github.com/luodian/otter) | Otter | 06/2023 | [code](https://github.com/luodian/otter) | arXiv |
| [**VideoLLM: Modeling Video Sequence with Large Language Models**](https://arxiv.org/abs/2305.13292)[](https://github.com/cg1177/videollm) | VideoLLM | 05/2023 | [code](https://github.com/cg1177/videollm) | arXiv |
#### Fine-tuning with Hybrid Adapters
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :-------: | :-----: | :------------------------------------------: | :---: |
| [**VTimeLLM: Empower LLM to Grasp Video Moments**](https://arxiv.org/abs/2311.18445v1)[](https://github.com/huangb23/vtimellm) | VTimeLLM | 11/2023 | [code](https://github.com/huangb23/vtimellm) | arXiv |
| [**GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation**](https://arxiv.org/abs/2311.16511v1) | GPT4Video | 11/2023 | - | arXiv |
### 🦾 Hybrid Methods
| Title | Model | Date | Code | Venue |
| :----------------------------------------------------------- | :-----------------: | :-----: | :----------------------------------------------------------: | :---: |
| [**VideoChat: Chat-Centric Video Understanding**](https://arxiv.org/abs/2305.06355)[](https://github.com/OpenGVLab/Ask-Anything) | VideoChat | 05/2023 | [code](https://github.com/OpenGVLab/Ask-Anything) [demo](https://huggingface.co/spaces/ynhe/AskAnything) | arXiv |
| [**PG-Video-LLaVA: Pixel Grounding Large Video-Language Models**](https://arxiv.org/abs/2311.13435v2)[](https://github.com/mbzuai-oryx/video-llava) | PG-Video-LLaVA | 11/2023 | [code](https://github.com/mbzuai-oryx/video-llava) | arXiv |
| [**TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding**](https://arxiv.org/abs/2312.02051)[](https://github.com/RenShuhuai-Andy/TimeChat) | TimeChat | 12/2023 | [code](https://github.com/RenShuhuai-Andy/TimeChat) | CVPR |
| [**Video-GroundingDINO: Towards Open-Vocabulary Spatio-Temporal Video Grounding**](https://arxiv.org/pdf/2401.00901.pdf)[](https://github.com/TalalWasim/Video-GroundingDINO) | Video-GroundingDINO | 12/2023 | [code](https://github.com/TalalWasim/Video-GroundingDINO) | arXiv |
| [**A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot**](https://aclanthology.org/2023.emnlp-main.608/) | Video4096 | 05/2023 | | EMNLP |
---
## Tasks, Datasets, and Benchmarks
#### Recognition and Anticipation
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
| **Charades** | [**Hollywood in homes: Crowdsourcing data collection for activity understanding**](https://arxiv.org/abs/1604.01753v3) | 2016 | [Link](http://vuchallenge.org/charades.html) | ECCV |
| **YouTube8M** | [**YouTube-8M: A Large-Scale Video Classification Benchmark**](https://arxiv.org/abs/1609.08675v1) | 2016 | [Link](https://research.google.com/youtube8m/download.html) | - |
| **ActivityNet** | [**ActivityNet: A Large-Scale Video Benchmark for Human Activity Understanding**](https://openaccess.thecvf.com/content_cvpr_2015/papers/Heilbron_ActivityNet_A_Large-Scale_2015_CVPR_paper.pdf) | 2015 | [Link](http://activity-net.org/) | CVPR |
| **Kinetics-GEBC** | [**GEB+: A Benchmark for Generic Event Boundary Captioning, Grounding and Retrieval**](https://arxiv.org/abs/2204.00486v4) | 2022 | [Link](https://github.com/showlab/geb-plus) | ECCV |
| **Kinetics-400** | [**The Kinetics Human Action Video Dataset**](https://arxiv.org/abs/1705.06950) | 2017 | [Link](https://paperswithcode.com/dataset/kinetics-400-1) | - |
| **VidChapters-7M** | [**VidChapters-7M: Video Chapters at Scale**](https://arxiv.org/abs/2309.13952) | 2023 | [Link](https://antoyang.github.io/vidchapters.html) | NeurIPS |
#### Captioning and Description
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
|**Microsoft Research Video Description Corpus (MSVD)**|[**Collecting Highly Parallel Data for Paraphrase Evaluation**](https://aclanthology.org/P11-1020.pdf)|2011|[Link](https://www.cs.utexas.edu/users/ml/clamp/videoDescription/#data)|ACL|
|**Microsoft Research Video-to-Text (MSR-VTT)**|[**MSR-VTT: A Large Video Description Dataset for Bridging Video and Language**](https://openaccess.thecvf.com/content_cvpr_2016/papers/Xu_MSR-VTT_A_Large_CVPR_2016_paper.pdf)|2016|[Link](https://www.microsoft.com/en-us/research/publication/msr-vtt-a-large-video-description-dataset-for-bridging-video-and-language/)|CVPR|
|**Tumblr GIF (TGIF)**|[**TGIF: A New Dataset and Benchmark on Animated GIF Description**](https://arxiv.org/abs/1604.02748v2)|2016|[Link](https://github.com/raingo/TGIF-Release)|CVPR|
|**Charades**|[**Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding**](https://arxiv.org/abs/1604.01753v3)|2016|[Link](https://prior.allenai.org/projects/charades)|ECCV|
|**Charades-Ego**|[**Actor and Observer: Joint Modeling of First and Third-Person Videos**](https://arxiv.org/abs/1804.0962)|2018|[Link](https://prior.allenai.org/projects/charades-ego)|CVPR|
|**ActivityNet Captions**|[**Dense-Captioning Events in Videos**](https://arxiv.org/abs/1705.00754)|2017|[Link](https://cs.stanford.edu/people/ranjaykrishna/densevid/)|ICCV|
|**HowTo100m**|[**HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips**](https://arxiv.org/abs/1906.03327)|2019|[Link](https://www.di.ens.fr/willow/research/howto100m/)|ICCV|
|**Movie Audio Descriptions (MAD)**|[**MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions**](https://arxiv.org/abs/2112.00431)|2021|[Link](https://github.com/Soldelli/MAD)|CVPR|
|**YouCook2**|[**Towards Automatic Learning of Procedures from Web Instructional Videos**](https://arxiv.org/abs/1703.09788)|2017|[Link](http://youcook2.eecs.umich.edu/)|AAAI|
|**MovieNet**|[**MovieNet: A Holistic Dataset for Movie Understanding**](https://arxiv.org/abs/2007.10937)|2020|[Link](https://movienet.github.io/)|ECCV|
|**Youku-mPLUG**|[**Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks**](https://arxiv.org/abs/2306.04362)|2023|[Link](https://github.com/X-PLUG/Youku-mPLUG)|arXiv|
|**Video Timeline Tags (ViTT)**|[**Multimodal Pretraining for Dense Video Captioning**](https://arxiv.org/abs/2011.11760)|2020|[Link](https://github.com/google-research-datasets/Video-Timeline-Tags-ViTT)|AACL-IJCNLP|
|**TVSum**|[**TVSum: Summarizing web videos using titles**](https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Song_TVSum_Summarizing_Web_2015_CVPR_paper.pdf)|2015|[Link](https://github.com/yalesong/tvsum)|CVPR|
|**SumMe**|[**Creating Summaries from User Videos**](https://www.semanticscholar.org/paper/Creating-Summaries-from-User-Videos-Gygli-Grabner/799bf307438ec2171e6f0bd5b8040f678d5b28da)|2014|[Link](http://www.vision.ee.ethz.ch/~gyglim/vsum/)|ECCV|
|**VideoXum**|[**VideoXum: Cross-modal Visual and Textural Summarization of Videos**](https://arxiv.org/abs/2303.12060)|2023|[Link](https://videoxum.github.io/)|IEEE Trans Multimedia|
|**Multi-Source Video Captioning (MSVC)**|[**VideoLLaMA2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs**](https://arxiv.org/abs/2406.07476)|2024|[Link](https://huggingface.co/datasets/DAMO-NLP-SG/Multi-Source-Video-Captioning)|arXiv|
#### Grounding and Retrieval
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
|**Epic-Kitchens-100**|[**Rescaling Egocentric Vision**](https://arxiv.org/abs/2006.13256v4)|2021|[Link](https://epic-kitchens.github.io/2021)|IJCV|
|**VCR (Visual Commonsense Reasoning)**|[**From Recognition to Cognition: Visual Commonsense Reasoning**](https://arxiv.org/abs/1811.10830v2)|2019|[Link](https://visualcommonsense.com/)|CVPR|
|**Ego4D-MQ and Ego4D-NLQ**|[**Ego4D: Around the World in 3,000 Hours of Egocentric Video**](https://ai.meta.com/research/publications/ego4d-unscripted-first-person-video-from-around-the-world-and-a-benchmark-suite-for-egocentric-perception/)|2021|[Link](https://ego4d-data.org/)|CVPR|
|**Vid-STG**|[**Where Does It Exist: Spatio-Temporal Video Grounding for Multi-Form Sentences**](https://arxiv.org/abs/2001.06891)|2020|[Link](https://github.com/Guaranteer/VidSTG-Dataset)|CVPR|
|**Charades-STA**|[**TALL: Temporal Activity Localization via Language Query**](https://arxiv.org/abs/1705.02101)|2017|[Link](https://github.com/jiyanggao/TALL)|ICCV|
|**DiDeMo**|[**Localizing Moments in Video with Natural Language**](https://arxiv.org/abs/1708.01641)|2017|[Link](https://github.com/LisaAnne/TemporalLanguageRelease)|ICCV|
#### Question Answering
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
|**MSVD-QA**|[**Video Question Answering via Gradually Refined Attention over Appearance and Motion**](https://dl.acm.org/doi/10.1145/3123266.3123427)|2017|[Link](https://github.com/xudejing/video-question-answering)|ACM Multimedia|
|**MSRVTT-QA**|[**Video Question Answering via Gradually Refined Attention over Appearance and Motion**](https://dl.acm.org/doi/10.1145/3123266.3123427)|2017|[Link](https://github.com/xudejing/video-question-answering)|ACM Multimedia|
|**TGIF-QA**|[**TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering**](https://arxiv.org/abs/1704.04497)|2017|[Link](https://github.com/YunseokJANG/tgif-qa)|CVPR|
|**ActivityNet-QA**|[**ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering**](https://arxiv.org/abs/1906.02467)|2019|[Link](https://github.com/MILVLG/activitynet-qa)|AAAI|
|**Pororo-QA**|[**DeepStory: Video Story QA by Deep Embedded Memory Networks**](https://arxiv.org/abs/1707.00836)|2017|[Link](https://github.com/Kyung-Min/PororoQA)|IJCAI|
|**TVQA**|[**TVQA: Localized, Compositional Video Question Answering**](https://arxiv.org/abs/1809.01696)|2018|[Link](https://tvqa.cs.unc.edu/)|EMNLP|
#### Video Instruction Tuning
##### Pretraining Dataset
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
|**VidChapters-7M**|[**VidChapters-7M: Video Chapters at Scale**](https://arxiv.org/abs/2309.13952)|2023|[Link](https://antoyang.github.io/vidchapters.html)|NeurIPS|
|**VALOR-1M**|[**VALOR: Vision-Audio-Language Omni-Perception Pretraining Model and Dataset**](https://arxiv.org/abs/2304.08345)|2023|[Link](https://github.com/TXH-mercury/VALOR)|arXiv|
|**Youku-mPLUG**|[**Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks**](https://arxiv.org/abs/2306.04362)|2023|[Link](https://github.com/X-PLUG/Youku-mPLUG)|arXiv|
|**InternVid**|[**InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation**](https://arxiv.org/abs/2307.06942)|2023|[Link](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid)|arXiv|
|**VAST-27M**|[**VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset**](https://arxiv.org/abs/2305.18500)|2023|[Link](https://github.com/TXH-mercury/VAST)|NeurIPS|
##### Fine-tuning Dataset
| Name | Paper | Date | Link | Venue |
| :----------------- | :----------------------------------------------------------: | :--: | :---------------------------------------------------------: | :-----: |
|**MIMIC-IT**|[**MIMIC-IT: Multi-Modal In-Context Instruction Tuning**](https://arxiv.org/abs/2306.05425)|2023|[Link](https://github.com/luodian/otter)|arXiv|
|**VideoInstruct100K**|[**Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models**](https://arxiv.org/pdf/2306.05424)|2023|[Link](https://huggingface.co/datasets/MBZUAI/VideoInstruct-100K)|arXiv|
|**TimeIT**|[**TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding**](https://arxiv.org/abs/2312.02051)|2023|[Link](https://huggingface.co/datasets/ShuhuaiRen/TimeIT)|CVPR|
#### Video-based Large Language Models Benchmark
| Title | Date | Code | Venue |
| :----------------------------------------------------------- | :-----: | :--------------------------------------------------------: | :------------------------------: |
| [**LVBench: An Extreme Long Video Understanding Benchmark**](https://arxiv.org/abs/2406.08035) | 06/2024 | [code](https://github.com/THUDM/LVBench) | - |
| [**Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models**](https://arxiv.org/abs/2311.16103) | 11/2023 | [code](https://github.com/PKU-YuanGroup/Video-Bench) | - |
| [**Perception Test: A Diagnostic Benchmark for Multimodal Video Models**](https://arxiv.org/abs/2305.13786) | 05/2023 | [code](https://github.com/google-deepmind/perception_test) | NeurIPS 2023, ICCV 2023 Workshop |
| [**Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks**](https://arxiv.org/abs/2306.04362v1) [](https://github.com/x-plug/youku-mplug) | 07/2023 | [code](https://github.com/x-plug/youku-mplug) | - |
| [**FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation**](https://arxiv.org/abs/2311.01813) [](https://github.com/llyx97/fetv) | 11/2023 | [code](https://github.com/llyx97/fetv) | NeurIPS 2023 |
| [**MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding**](https://arxiv.org/abs/2312.04817) | 12/2023 | [code](https://github.com/OpenGVLab/MoVQA) | - |
| [**MVBench: A Comprehensive Multi-modal Video Understanding Benchmark**](https://arxiv.org/abs/2311.17005) | 12/2023 | [code](https://github.com/OpenGVLab/Ask-Anything) | - |
| [**TempCompass: Do Video LLMs Really Understand Videos?**](https://arxiv.org/abs/2403.00476) [](https://github.com/llyx97/TempCompass) | 03/2024 | [code](https://github.com/llyx97/TempCompass) | ACL 2024 |
| [**Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis**](https://arxiv.org/abs/2405.21075) [](https://github.com/BradyFU/Video-MME) | 06/2024 | [code](https://github.com/BradyFU/Video-MME) | - |
## Contributing
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!
### 📑 Citation
If you find our survey useful for your research, please cite the following paper:
```bibtex
@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},
}
```
### 🌟 Star History
[](https://star-history.com/#yunlong10/Awesome-LLMs-for-Video-Understanding&Date)
### ♥️ Contributors
