mlsanityData to 7 Oct 2026

ChineseVideoBench: Benchmarking Multi-modal Large Models for Chinese Video Question Answering

arXiv 2511.18399 ↗First posted 23 Nov 2025cs.CVcited by 4

On the map: Multimodal LLMs / vision-language models → Video understanding with MLLMs

Abstract (arXiv)

This paper introduces ChineseVideoBench, a pioneering benchmark specifically designed for evaluating Multimodal Large Language Models (MLLMs) in Chinese Video Question Answering. The growing demand for sophisticated video analysis capabilities highlights the critical need for comprehensive, culturally-aware evaluation frameworks. ChineseVideoBench addresses this gap by providing a robust dataset and tailored evaluation metrics, enabling rigorous assessment of state-of-the-art MLLMs on complex Chinese video content. Specifically, ChineseVideoBench comprises 8 main classes and 12 sub-classes, encompassing tasks that demand both deep video understanding and nuanced Chinese linguistic and cultural awareness. Our empirical evaluations reveal that ChineseVideoBench presents a significant challenge to current MLLMs. Among the models assessed, Gemini 2.5 Pro achieves the highest performance with an overall score of 77.9%, while InternVL-38B emerges as the most competitive open-source model.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Video understanding with MLLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 33 of its 39 references that are in the map. First posted on arXiv 23 Nov 2025.

Builds on

33 of 39 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024MMBench-Video: A Long-Form Multi-Shot Benchmark for Holistic Video Understanding20 Jun
  2. 2024Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis31 May
  3. 2024Elysium: Exploring Object-level Perception in Videos via MLLM25 Mar
  4. 2023MVBench: A Comprehensive Multi-modal Video Understanding Benchmark28 Nov
  5. 2023Video-LLaVA: Learning United Visual Representation by Alignment Before Projection16 Nov
  6. 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding5 Jun

Other references · 27

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2023Visual Instruction Tuningcited by 7,792
  4. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  5. 2025Qwen2.5-VL Technical Reportcited by 5,418
  6. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  7. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  8. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
  9. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  10. 2024LLaVA-OneVision: Easy Visual Task Transfercited by 2,669
  11. 2024Qwen2 Technical Reportcited by 2,076
  12. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
15 more
  1. 2024ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Toolscited by 1,096
  2. 2023Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Modelscited by 1,047
  3. 2023VILA: On Pre-training for Visual Language Modelscited by 686
  4. 2023OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Modelscited by 560
  5. 2024LLaVA-Video: Video Instruction Tuning With Synthetic Datacited by 550
  6. 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 443
  7. 2023Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understandingcited by 368
  8. 2025Seed1.5-VL Technical Reportcited by 367
  9. 2024TempCompass: Do Video LLMs Really Understand Videos?cited by 352
  10. 2015MovieQA: Understanding Stories in Movies through Question-Answeringcited by 304
  11. 2024NVILA: Efficient Frontier Visual Language Modelscited by 206
  12. 2023Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Modelscited by 107
  13. 2016MarioQA: Answering Questions by Watching Gameplay Videoscited by 42
  14. 2025Vision as LoRAcited by 37
  15. 2024Dynamic-VLM: Simple Dynamic Visual Token Compression for VideoLLMcited by 31

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026Transmittance-Guided Structure-Texture Decomposition for Nighttime Image Dehazingcited by 0
  2. 2026Hierarchical Awareness Adapters with Hybrid Pyramid Feature Fusion for Dense Depth Predictioncited by 0
  3. 2026Multi-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusioncited by 0
  4. 2026Cultural Moment Benchmark: Evaluating Video Cultural Reasoning and Grounding in Southeast Asiacited by 0

Similar, not cited

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  1. 2025An Empirical Study for Representations of Videos in Video Question Answering via MLLMs
  2. 2025VideoVista-CulturalLingo: 360^circ Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension
  3. 2025Multi-TW: Benchmarking Multimodal Models on Traditional Chinese Question Answering in Taiwan
  4. 2025TCC-Bench: Benchmarking the Traditional Chinese Culture Understanding Capabilities of MLLMs
  5. 2024LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
  6. 2025TextVidBench: A Benchmark for Long Video Scene Text Understanding
  7. 2024Lost in Time: A New Temporal Benchmark for VideoLLMs
  8. 2025VisTW: Benchmarking Vision-Language Models for Traditional Chinese in Taiwan