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About this paper
Qiucheng Wu, Handong Zhao, Michael Saxon, Trung Bui, William Yang Wang, Yang Zhang, Shiyu Chang
Researchers introduce VSP, a benchmark for evaluating Vision Language Models on visual spatial planning tasks. Experiments show that current state-of-the-art VLMs exhibit sub-optimal performance, with visual perception identified as a major bottleneck limiting their ability to comprehend spatial arrangements and devise multi-step action plans.
Key insights
- Current state-of-the-art VLMs perform sub-optimally in visual spatial planning tasks, with performance significantly declining as task complexity increases.
- Visual perception presents a major bottleneck for VLMs, as converting visual inputs to textual descriptions substantially improves planning performance.
- While perception is critical, deficiencies also exist in spatial reasoning, and in-context learning offers only minor, inconsistent improvements for complex spatial planning tasks.
Summary by alphaXiv.
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