A Very Big Video Reasoning Suite
Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thadd\"aus Wiedemer, Qingying Gao, Dezhi Luo, Yaoyao Qian, Lianyu Huang, Zelong Hong, Jiahui Ge, Qianli Ma, Hang He, Yifan Zhou, Lingzi Guo, Lantao Mei, Jiachen Li, Hanwen Xing, Tianqi Zhao, Fengyuan Yu, Weihang Xiao, Yizheng Jiao

TL;DR
This paper introduces the Very Big Video Reasoning (VBVR) Dataset and VBVR-Bench, large-scale resources for evaluating and understanding video reasoning capabilities, enabling systematic scaling studies and fostering progress in generalizable video reasoning.
Contribution
The paper presents the first large-scale video reasoning dataset and benchmark, facilitating systematic evaluation and scaling studies of video reasoning models.
Findings
Early signs of emergent generalization to unseen reasoning tasks.
The VBVR suite enables reproducible and interpretable diagnosis of video reasoning.
The dataset spans over one million video clips across 200 reasoning tasks.
Abstract
Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over spatiotemporal structure such as continuity, interaction, and causality. However, systematically studying video reasoning and its scaling behavior is hindered by the lack of large-scale training data. To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Human Pose and Action Recognition · Visual Attention and Saliency Detection
