MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating
  Multimodal LLMs in Multi-Turn Dialogues
The recent development of Multimodal Large Language Models (MLLMs) has
significantly advanced AI's ability to understand visual modalities. However,
existing evaluation benchmarks remain limited to single-turn question
answering, overlooking the complexity of multi-turn dialogues in real-world
scenarios. To bridge this gap, we introduce MT-Video-Bench, a holistic video
understanding benchmark for evaluating MLLMs in multi-turn dialogues.
Specifically, our MT-Video-Bench mainly assesses six core competencies that
focus on perceptivity and interactivity, encompassing 987 meticulously curated
multi-turn dialogues from diverse domains. These capabilities are rigorously
aligned with real-world applications, such as interactive sports analysis and
multi-turn video-based intelligent tutoring. With MT-Video-Bench, we
extensively evaluate various state-of-the-art open-source and closed-source
MLLMs, revealing their significant performance discrepancies and limitations in
handling multi-turn video dialogues. The benchmark will be publicly available
to foster future research.
68f6f0d624c4489363111882
Yaning Pan
68f6f0d624c4489363111883
Zekun Wang
68f6f0d624c4489363111884
Qianqian Xie
68f6f0d624c4489363111885
Yongqian Wen
68f6f0d624c4489363111886
Yuanxing Zhang
68f6f0d624c4489363111887
Guohui Zhang
68f6f0d624c4489363111888
Haoxuan Hu
claimed_verified
2025-10-22T15:46:03.474Z
68f8792d603c18f07bf357b2
/avatars/7622804ccc0c4afc90a2956661eb84af.svg
Haoxuan Hu
user
HarrisonHu
68f6f0d624c4489363111889
Zhiyu Pan
68f6f0d624c448936311188a
Yibing Huang
claimed_verified
2025-10-23T15:00:54.655Z
68f8777aa094de21c72e60a0
/avatars/72bce54d9c3634ae27bfd7f7bf983e12.svg
Yibing Huang
user
nassi2e123
68f6f0d624c448936311188b
Zhidong Gan
68f6f0d624c448936311188c
Yonghong Lin
68f6f0d624c448936311188d
An Ping
68f6f0d624c448936311188e
Tianhao Peng
68f6f0d624c448936311188f
Jiaheng Liu
MT-Video-Bench: A Holistic Video Understanding Benchmark for Evaluating
  Multimodal LLMs in Multi-Turn Dialogues
The recent development of Multimodal Large Language Models (MLLMs) has
significantly advanced AI's ability to understand visual modalities. However,
existing evaluation benchmarks remain limited to single-turn question
answering, overlooking the complexity of multi-turn dialogues in real-world
scenarios. To bridge this gap, we introduce MT-Video-Bench, a holistic video
understanding benchmark for evaluating MLLMs in multi-turn dialogues.
Specifically, our MT-Video-Bench mainly assesses six core competencies that
focus on perceptivity and interactivity, encompassing 987 meticulously curated
multi-turn dialogues from diverse domains. These capabilities are rigorously
aligned with real-world applications, such as interactive sports analysis and
multi-turn video-based intelligent tutoring. With MT-Video-Bench, we
extensively evaluate various state-of-the-art open-source and closed-source
MLLMs, revealing their significant performance discrepancies and limitations in
handling multi-turn video dialogues. The benchmark will be publicly available
to foster future research.
MT-Video-Bench evaluates MLLMs in multi-turn video dialogues, assessing perceptivity and interactivity across diverse domains.
Multimodal Large Language Models MLLMs video understanding benchmark multi-turn dialogues perceptivity interactivity interactive sports analysis multi-turn video-based intelligent tutoring