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OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models
arXiv – CS AI|Mengdi Jia, Zekun Qi, Shaochen Zhang, Wenyao Zhang, Xinqiang Yu, Jiawei He, He Wang, Li Yi||3 views
🤖AI Summary
Researchers introduce OmniSpatial, a comprehensive benchmark for testing spatial reasoning capabilities in vision-language models (VLMs). The benchmark reveals significant limitations in both open and closed-source VLMs across four major spatial reasoning categories, with over 8,400 question-answer pairs testing advanced cognitive abilities.
Key Takeaways
- →OmniSpatial benchmark exposes major gaps in current vision-language models' spatial reasoning abilities beyond basic left-right distinctions.
- →The benchmark covers four categories: dynamic reasoning, complex spatial logic, spatial interaction, and perspective-taking with 50 subcategories.
- →Both open-source and closed-source VLMs show significant limitations in comprehensive spatial reasoning tasks.
- →Researchers propose PointGraph and SpatialCoT strategies to improve spatial reasoning capabilities.
- →Current VLMs have largely saturated performance on elementary spatial tasks but struggle with advanced cognitive reasoning.
#vision-language-models#spatial-reasoning#ai-benchmarks#cognitive-psychology#vlm-evaluation#arxiv#ai-research
Read Original →via arXiv – CS AI
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