CVBench: Evaluating Cross-Video Synergies for Complex Multimodal Understanding and Reasoning

Data Overview

CVBench is the first benchmark specifically designed to evaluate cross-video reasoning capabilities in Multimodal Large Language Models (MLLMs). While existing benchmarks focus on single-video tasks, CVBench addresses real-world challenges that demand understanding relationships between multiple video streams—such as multi-camera surveillance, procedural learning, and multi-angle activity analysis.

CVBench includes 1,000 high-quality question-answer (QA) pairs across 1,315 videos, spanning 15 task types and 5 diverse video domains. The tasks are structured around fifteen core categories:

• Cross-video Anomaly Detection
• Cross-video Scene Recognition
• Multi-video Key-Action Recognition
• Cross-video Event Retrieval
• Cross-video Object Recognition
• Multi-video Attribute Recognition
• Joint-video Counting
• Cross-video Entity Matching
• Multi-view Scene Understanding
• Multi-video Temporal Reasoning
• Joint-video Spatial Navigating
• Video Difference Caption
• Cross-video Counterfactual Reasoning
• Joint-video Summarization
• Cross-video Procedural Transfer

This benchmark is designed to probe inter-video context retention, event disambiguation, and commonsense-driven reasoning across asynchronous visual streams.

• Technical Report (arXiv)(Coming Soon)

Data Distribution

CVBench samples are built from domain-diverse clusters including:

• Sports (e.g., basketball or football)
• Life records (e.g., cooking or life records)
• Surveillance (e.g., anomaly detection or matching)
• Tutorials
• Cartoons
• news

Data Fields

How to Use

You can load the dataset using the datasets library:

from datasets import load_dataset

cvbench = load_dataset("Dongyh35/CVBench", "test")

Dataset Characterization

Data Collection Method
Hybrid: Task-driven video scraping + LLM-assisted QA generation

Annotation Method
Segment-level captioning → Inter-video summarization → QA synthesis → Human verification

Video Sources
Videos were sourced from YouTube and selected based on their relevance to real-world cross-video tasks. The dataset emphasizes diversity, clarity, and practicality.

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Benchmark Performance

We evaluated 10+ leading MLLMs, including GPT-4o, Gemini 1.5, Qwen2.5-VL, and Video-R1.

Despite advances, most models show difficulty in context retention and event disambiguation, particularly when more than 2 videos are involved.

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License / Terms of Use

CVBench is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0). We only allow the use of this dataset for academic research and education purpose. You can view the license here: https://creativecommons.org/licenses/by/4.0/

Data Developer: Anonymous Authors (AAAI 2026 submission)

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Use Cases

• Training MLLMs for procedural reasoning across asynchronous videos
• Evaluating multi-camera or multi-view visual understanding
• Diagnostic tasks for context alignment and event coherence

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Release Date

May 2025

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Data Version

1.0 (05/2025)

1.1 (07/2025), fixed errors or missing issues

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Intended Use

CVBench is designed to drive forward the frontier of multi-video understanding, a key challenge for next-generation AI systems. It offers realistic evaluation for use cases ranging from intelligent surveillance to educational media comprehension.

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Ethical Considerations

Due to its use of publicly available YouTube videos, efforts were made to avoid sensitive or private content. Videos were manually filtered for suitability, and human-in-the-loop verification was conducted to ensure question relevance and fairness.

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Citation

If you use CVBench in your research, please cite:

@article{cvbench2025,
  title={CVBench: Evaluating Cross-Video Synergies for Complex Multimodal Understanding and Reasoning},
  author={Anonymous},
  journal={arxiv},
  year={2025},
  url={Soon}
}