PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
📰 ArXiv cs.AI
PerceptionComp is a benchmark for complex video reasoning that requires multiple pieces of visual evidence and compositional constraints
Action Steps
- Design a video benchmark with manually annotated data
- Develop models that can handle long-horizon, perception-centric video reasoning
- Evaluate models using PerceptionComp to assess their ability to integrate multiple pieces of visual evidence and compositional constraints
Who Needs to Know This
AI engineers and researchers working on computer vision and multimodal reasoning tasks can benefit from PerceptionComp to evaluate and improve their models' performance on complex perception-centric reasoning
Key Insight
💡 PerceptionComp requires models to integrate multiple pieces of visual evidence and compositional constraints to answer questions
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📹 Introducing PerceptionComp: a benchmark for complex video reasoning! 🤖
Key Takeaways
PerceptionComp is a benchmark for complex video reasoning that requires multiple pieces of visual evidence and compositional constraints
Full Article
Title: PerceptionComp: A Video Benchmark for Complex Perception-Centric Reasoning
Abstract:
arXiv:2603.26653v1 Announce Type: cross 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,
Abstract:
arXiv:2603.26653v1 Announce Type: cross 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,
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