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Understanding Space Is Rocket Science -- Only Top Reasoning Models Can Solve Spatial Understanding Tasks
Created by
Haebom
Author
Nils Hoehing, Mayug Maniparambil, Ellen Rushe, Noel E. O'Connor, Anthony Ventresque
Outline
RocketScience is an open-source, contrastive VLM benchmark designed to evaluate spatial relation understanding. It consists of novel real-world image-text pairs, primarily focusing on relative spatial understanding and object ordering. Designed to be easy for humans but challenging for current VLM models, it is experimentally validated. The results demonstrate the shortcomings of open-source and state-of-the-art commercial VLMs in spatial relation understanding, while demonstrating the surprisingly high performance of inference models. Furthermore, we performed an analysis to separate the contributions of object localization and spatial reasoning in a thought chain-based model, finding that the benchmark performance is limited by spatial reasoning, not object localization. The dataset is released under a CC-BY-4.0 license, and the evaluation code is available at https://github.com/nilshoehing/rocketscience .
Experimentally demonstrating that current VLM models struggle to understand spatial relationships.
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We reveal that spatial reasoning ability is a major bottleneck in VLM performance.
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Provides RocketScience, a new benchmark for assessing spatial relationship understanding.
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Confirming the high spatial inference ability of the inference model.
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Enabling research by providing open datasets and evaluation code.
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Limitations:
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Benchmarks may focus only on understanding specific types of spatial relationships and may not fully assess general spatial reasoning abilities.
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Although it clearly demonstrates the limitations of current VLM, the suitability of the benchmark should be continuously reviewed as VLM evolves in the future.
