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Computer Science > Computer Vision and Pattern Recognition

arXiv:2603.26653 (cs)
[Submitted on 27 Mar 2026]

Title:PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning

Authors:Shaoxuan Li, Zhixuan Zhao, Hanze Deng, Zirun Ma, Shulin Tian, Zuyan Liu, Yushi Hu, Haoning Wu, Yuhao Dong, Benlin Liu, Ziwei Liu, Ranjay Krishna
View a PDF of the paper titled PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning, by Shaoxuan Li and 11 other authors
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Abstract:We introduce PerceptionComp, a manually annotated benchmark for complex, long-horizon, perception-centric video reasoning. PerceptionComp is designed so that no single moment is sufficient: answering each question requires multiple temporally separated pieces of visual evidence and compositional constraints under conjunctive and sequential logic, spanning perceptual subtasks such as objects, attributes, relations, locations, actions, and events, and requiring skills including semantic recognition, visual correspondence, temporal reasoning, and spatial reasoning. The benchmark contains 1,114 highly complex questions on 279 videos from diverse domains including city walk tours, indoor villa tours, video games, and extreme outdoor sports, with 100% manual annotation. Human studies show that PerceptionComp requires substantial test-time thinking and repeated perception steps: participants take much longer than on prior benchmarks, and accuracy drops to near chance (18.97%) when rewatching is disallowed. State-of-the-art MLLMs also perform substantially worse on PerceptionComp than on existing benchmarks: the best model in our evaluation, Gemini-3-Flash, reaches only 45.96% accuracy in the five-choice setting, while open-source models remain below 40%. These results suggest that perception-centric long-horizon video reasoning remains a major bottleneck, and we hope PerceptionComp will help drive progress in perceptual reasoning.
Comments: Project Page: this https URL
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2603.26653 [cs.CV]
  (or arXiv:2603.26653v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2603.26653
arXiv-issued DOI via DataCite

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From: Benlin Liu [view email]
[v1] Fri, 27 Mar 2026 17:54:36 UTC (10,717 KB)
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