MLVU: Multi-task Long Video Understanding Benchmark

This repo contains the annotation data and evaluation code for the paper "MLVU: A Comprehensive Benchmark for Multi-Task Long Video Understanding".

🔔 News:

• 📢 11/3/2024: Note: We apologize for the temporary downtime of the Test set evaluation website. With the CVPR deadline approaching, we recommend using lmmseval for quick, one-click evaluations on the MLVU-Dev set. For the MLVU-Test set, we will assist with evaluations via email during this period. Please format your Test results according to the guidelines and send them to zhoujunjie[#]bupt.edu.cn or 570533048[#]qq.com. If you want your model to appear on the leaderboard, feel free to contact us. (Please replace # with [at]. We typically respond within one day.) 🔥
• 🚀 8/23/2024: The online website for the MLVU-Test set evaluation has been released (Link), welcome to submit your model and result! 🔥
• 🆕 7/28/2024: The data for the MLVU-Test set has been released (🤗 Link)! The test set includes 11 different tasks, featuring our newly added Sports Question Answering (SQA, single-detail LVU) and Tutorial Question Answering (TQA, multi-detail LVU). The MLVU-Test has expanded the number of options in multiple-choice questions to six. While the ground truth of the MLVU-Test will remain undisclosed, everyone will be able to evaluate online (the online website will be coming soon!). 🔥
• 🎉 7/28/2024: The MLVU-Dev set has now been integrated into lmms-eval! You can now conveniently evaluate the multiple-choice questions (MLVU_M) of the MLVU-Dev set with a single click using lmms-eval. Thanks to the lmms-eval team! 🔥
• 🏆 7/25/2024: We have released the MLVU-Test leaderboard and incorporated evaluation results for several recently launched models, such as LongVA, VILA, ShareGPT4-Video, etc. 🔥
• 🏠 6/19/2024: For better maintenance and updates of MLVU, we have migrated MLVU to this new repository. We will continue to update and maintain MLVU here. If you have any questions, feel free to raise an issue. 🔥
• 🥳 6/7/2024: We have released the MLVU Benchmark and Paper! 🔥

License

Our dataset is under the CC-BY-NC-SA-4.0 license.

⚠️ If you need to access and use our dataset, you must understand and agree: This dataset is for research purposes only and cannot be used for any commercial or other purposes. The user assumes all effects arising from any other use and dissemination.

We do not own the copyright of any raw video files. Currently, we provide video access to researchers under the condition of acknowledging the above license. For the video data used, we respect and acknowledge any copyrights of the video authors. Therefore, for the movies, TV series, documentaries, and cartoons used in the dataset, we have reduced the resolution, clipped the length, adjusted dimensions, etc. of the original videos to minimize the impact on the rights of the original works.

If the original authors of the related works still believe that the videos should be removed, please contact [email protected] or directly raise an issue.

Introduction

We introduce MLVU: the first comprehensive benchmark designed for evaluating Multimodal Large Language Models (MLLMs) in Long Video Understanding (LVU) tasks. MLVU is constructed from a wide variety of long videos, with lengths ranging from 3 minutes to 2 hours, and includes nine distinct evaluation tasks. These tasks challenge MLLMs to handle different types of tasks, leveraging both global and local information from videos.

Our evaluation of 20 popular MLLMs, including GPT-4o, reveals significant challenges in LVU, with even the top-performing GPT-4o only achieving an average score of 64.6% in multi-choice tasks. In addition, our empirical results underscore the need for improvements in context length, image understanding, and strong LLM-backbones. We anticipate that MLVU will serve as a catalyst for the community to further advance MLLMs' capabilities in understanding long videos.

🏆 Mini-Leaderboard (MLVU Dev Set)

Model	Input	Size	M-Avg	G-Avg
Full mark	-	-	100	10
Oryx-1.5	128 frm	32B	72.3	--
Aria	256 frm	25B	70.6	5.02
LLaVA-OneVision	32 frm	72B	66.4	--
Video-XL	256 frm	7B	64.9	4.50
GPT-4o	0.5 fps	-	64.6	5.80
TimeMarker	<=128 frm	8B	63.9	3.99
Video-CCAM	96 frm	14B	63.1	4.01
VideoLLaMA2	16 frm	72B	61.2	--
InternVL2	16 frm	76B	59.9	--
VILA-1.5	14 frm	40B	56.7	4.31
LongVA	256 frm	7B	56.3	4.33
InternVL-1.5	16 frm	26B	50.4	4.02
GPT-4 Turbo	16 frm	-	49.2	5.35
VideoLLaMA2-Chat	16 frm	7B	48.5	3.99
VideoChat2_HD	16 frm	7B	47.9	3.99
Video-LLaVA	8 frm	7B	47.3	3.84
ShareGPT4Video	16 frm	8B	46.4	3.77
VideoChat2-Vicuna	16 frm	7B	44.5	3.81
MiniGPT4-Video	90 frm	7B	44.5	3.36
Qwen-VL-Max	16 frm	-	42.2	3.96
VTimeLLM	100 frm	7B	41.9	3.94
LLaVA-1.6	16 frm	7B	39.3	3.23
Claude-3-Opus	16 frm	-	36.5	3.39
MA-LMM	1000 frm	7B	36.4	3.46
Video-LLaMA-2	16 frm	13B	35.5	3.78
LLaMA-VID	1 fps	7B	33.2	4.22
Video-ChatGPT	100 frm	7B	31.3	3.90
TimeChat	96 frm	7B	30.9	3.42
VideoChat	16 frm	7B	29.2	3.66
Movie-LLM	1 fps	7B	26.1	3.94
mPLUG-Owl-V	16 frm	7B	25.9	3.84
MovieChat	2048 frm	7B	25.8	2.78
Otter-V	16 frm	7B	24.4	3.31
Otter-I	16 frm	7B	23.3	3.15

🏆 MLVU-Test Leaderboard

This table is sorted by M-AVG in descending order. * means the proprietary models.

	Input	Size	TR	AR	NQA	ER	PQA	SQA	AO	AC	TQA	M-AVG	SSC	VS	G-Avg
Aria	256 frm	25B	86.8	64.1	75.0	56.6	66.0	58.3	48.6	25.0	46.5	58.5	--	--	--
GPT-4o*	0.5 fps	--	83.7	68.8	42.9	47.8	57.1	63.6	46.2	35.0	48.7	54.9	6.80	4.94	5.87
TimeMarker	128 frm	8B	85.7	53.9	65.0	49.1	52.0	41.7	31.4	26.7	37.2	49.2	4.02	3.20	3.61
LLaVA-OneVision	32 frm	72B	83.5	56.4	46.7	58.4	58.0	27.8	35.7	23.3	34.9	47.2	5.09	3.75	4.42
InternVL2	16 frm	76B	85.7	51.3	48.3	47.2	52.0	44.4	32.9	15.0	34.9	45.7	5.25	2.55	3.90
VideoLLaMA2	16 frm	72B	80.2	53.8	36.7	54.7	54.0	38.9	42.9	16.7	32.6	45.6	5.09	2.80	3.95
Video-XL	256 frm	7B	78.0	28.2	50.0	41.5	46.0	41.6	48.6	31.7	44.2	45.5	5.02	3.40	4.21
VILA-1.5	14 frm	40B	84.7	56.4	38.3	35.8	62.0	38.8	34.3	11.7	34.9	44.2	5.11	2.53	3.82
TS-LLaVA	50 frm	34B	83.5	43.6	55.0	32.1	46.0	55.6	28.6	10.0	32.6	43.0	--	--	--
Video-CCAM	96 frm	14B	79.1	38.5	45.0	52.8	56.0	33.3	24.3	26.7	30.2	42.9	4.49	2.65	3.57
LongVA	256 frm	7B	81.3	41.0	46.7	39.6	46.0	44.4	17.1	23.3	30.2	41.1	4.92	2.90	3.91
InternVL-1.5	16 frm	26B	80.2	51.3	40.0	24.5	42.0	30.6	14.3	13.3	39.5	37.3	5.18	2.73	3.96
VideoChat2_HD	16 frm	7B	74.7	43.6	35.0	34.0	30.0	30.6	21.4	23.3	23.3	35.1	5.14	2.83	3.99
VideoLLaMA2-Chat	16 frm	7B	76.9	35.9	26.7	34.0	40.0	27.8	17.1	15.0	20.9	32.7	5.27	2.40	3.84
ShareGPT4Video	16 frm	8B	73.6	25.6	31.7	45.3	38.0	38.9	17.1	8.3	25.6	33.8	4.72	2.53	3.63
VideoChat2-Vicuna	16 frm	7B	72.5	30.8	18.3	28.3	26.0	36.1	17.1	23.3	18.6	30.1	4.80	2.30	3.55
Video-LLaVA	8 frm	7B	70.3	38.5	13.3	26.4	26.0	38.9	20.0	21.7	20.9	30.7	5.06	2.30	3.68
LLaVA-1.6	16 frm	7B	63.7	17.9	13.3	26.4	30.0	22.2	21.4	16.7	16.3	25.3	4.20	2.00	3.10
Claude-3-Opus*	16 frm	--	53.8	30.8	14.0	17.0	20.0	47.2	10.0	6.7	25.6	25.0	3.67	2.83	3.25
VideoChat	16 frm	7B	26.4	12.8	18.3	17.0	22.0	11.1	15.7	11.7	14.0	16.6	4.90	2.15	3.53
Video-ChatGPT	16 frm	7B	17.6	17.9	28.3	32.1	22.0	27.8	17.1	13.3	11.6	20.9	5.06	2.22	3.64
Video-LLaMA-2	16 frm	13B	52.7	12.8	13.3	17.0	12.0	19.4	15.7	8.3	18.6	18.9	4.87	2.23	3.55
Qwen-VL-Max*	10 frm	--	75.8	53.8	15.0	26.4	38.0	44.4	20.0	11.7	22.6	34.2	4.84	3.00	3.92
MA-LMM	16 frm	7B	44.0	23.1	13.3	30.2	14.0	27.8	18.6	13.3	14.0	22.0	4.61	3.04	3.83
MiniGPT4-Video	90 frm	7B	64.9	46.2	20.0	30.2	30.0	16.7	15.7	15.0	18.6	28.6	4.27	2.50	3.39
Movie-LLM	1 fps	7B	27.5	25.6	10.0	11.3	16.0	16.7	20.0	21.7	23.3	19.1	4.93	2.10	3.52
Otter-I	16 frm	7B	17.6	17.9	16.7	17.0	18.0	16.7	15.7	16.7	14.0	16.7	3.90	2.03	2.97
Otter-V	16 frm	7B	16.5	12.8	16.7	22.6	22.0	8.3	12.9	13.3	16.3	15.7	4.20	2.18	3.19
MovieChat	2048 frm	7B	18.7	10.3	23.3	15.1	16.0	30.6	17.1	15.0	16.3	18.0	3.24	2.30	2.77
mPLUG-Owl-V	16 frm	7B	25.3	15.4	6.7	13.2	22.0	19.4	14.3	20.0	18.6	17.2	5.01	2.20	3.61
LLaMA-VID	1 fps	7B	20.9	23.1	21.7	11.3	16.0	16.7	18.6	15.0	11.6	17.2	4.15	2.70	3.43

License

Our dataset is under the CC-BY-NC-SA-4.0 license.

⚠️ If you need to access and use our dataset, you must understand and agree: This dataset is for research purposes only and cannot be used for any commercial or other purposes. The user assumes all effects arising from any other use and dissemination.

We do not own the copyright of any raw video files. Currently, we provide video access to researchers under the condition of acknowledging the above license. For the video data used, we respect and acknowledge any copyrights of the video authors. Therefore, for the movies, TV series, documentaries, and cartoons used in the dataset, we have reduced the resolution, clipped the length, adjusted dimensions, etc. of the original videos to minimize the impact on the rights of the original works.

If the original authors of the related works still believe that the videos should be removed, please contact [email protected] or directly raise an issue.

MLVU Benchmark

Before you access our dataset, we kindly ask you to thoroughly read and understand the license outlined above. If you cannot agree to these terms, we request that you refrain from downloading our video data.

The annotation file is readily accessible here. For the raw videos, you can access them via this 🤗 HF Link.

MLVU encompasses nine distinct tasks, which include multiple-choice tasks as well as free-form generation tasks. These tasks are specifically tailored for long-form video understanding, and are classified into three categories: holistic understanding, single detail understanding, and multi-detail understanding. Examples of the tasks are displayed below.

Evaluation

Please refer to our evaluation and evaluation_test folder for more details.

Hosting and Maintenance

The annotation files will be permanently retained.

If some videos are requested to be removed, we will replace them with a set of video frames sparsely sampled from the video and adjusted in resolution. Since all the questions in MLVU are only related to visual content and do not involve audio, this will not significantly affect the validity of MLVU (most existing MLLMs also understand videos by frame extraction).

If even retaining the frame set is not allowed, we will still keep the relevant annotation files, and replace them with the meta-information of the video, or actively seek more reliable and risk-free video sources.

Citation

If you find this repository useful, please consider giving a star ⭐ and citation

@article{MLVU,
  title={MLVU: A Comprehensive Benchmark for Multi-Task Long Video Understanding},
  author={Zhou, Junjie and Shu, Yan and Zhao, Bo and Wu, Boya and Xiao, Shitao and Yang, Xi and Xiong, Yongping and Zhang, Bo and Huang, Tiejun and Liu, Zheng},
  journal={arXiv preprint arXiv:2406.04264},
  year={2024}
}