MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models. However, most benchmarks predominantly assess spatial understanding in the static image tasks, while overlooking temporal understanding in the dynamic video tasks. To alleviate this issue, we introduce a comprehensive Multi-modal Video understanding Benchmark, namely MVBench, which covers 20 challenging video tasks that cannot be effectively solved with a single frame. Specifically, we first introduce a novel static-to-dynamic method to define these temporal-related tasks. By transforming various static tasks into dynamic ones, we enable the systematic generation of video tasks that require a broad spectrum of temporal skills, ranging from perception to cognition. Then, guided by the task definition, we automatically convert public video annotations into multiple-choice QA to evaluate each task. On one hand, such a distinct paradigm allows us to build MVBench efficiently, without much manual intervention. On the other hand, it guarantees evaluation fairness with ground-truth video annotations, avoiding the biased scoring of LLMs. Moreover, we further develop a robust video MLLM baseline, i.e., VideoChat2, by progressive multi-modal training with diverse instruction-tuning data. The extensive results on our MVBench reveal that, the existing MLLMs are far from satisfactory in temporal understanding, while our VideoChat2 largely surpasses these leading models by over 15% on MVBench. All models and data are available at https://github.com/OpenGVLab/Ask-Anything.
PDF AbstractCode
Spaces
Tasks
Fairness
Multiple-choice
Video-based Generative Performance Benchmarking
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
Video-based Generative Performance Benchmarking (Correctness of Information)
Video-based Generative Performance Benchmarking (Detail Orientation))
Video-based Generative Performance Benchmarking (Temporal Understanding)
Video Question Answering
Video Understanding
Zero-Shot Learning
Zero-Shot Video Question Answer
Datasets
Introduced in the Paper:
MVBenchUsed in the Paper:
ImageNet
MS COCO
Kinetics
CLEVR
GQA
OK-VQA
TextVQA
Charades-STA
WebVid
TVQA
DocVQA
A-OKVQA
MMBench
MiT
ActivityNet-QA
NExT-QA
ST-VQA
TextCaps
MSRVTT-QA
SEED-Bench
MSVD-QA
YouCook
MovieNet
VLN-CE
VisualMRC
VideoInstruct
STAR Benchmark
ViQuAE
Perception Test
FunQA
