cambridgeltl/visual-spatial-reasoning
[TACL'23] VSR: A probing benchmark for spatial undersranding of vision-language models.
This project offers a specialized dataset for evaluating how well AI models understand spatial relationships between objects in images. It takes image-caption pairs describing spatial arrangements (e.g., "The cat is behind the laptop") and checks if an AI can correctly determine if the description is true or false. Researchers and practitioners developing or benchmarking vision-language AI models would use this to precisely diagnose spatial reasoning capabilities.
140 stars. No commits in the last 6 months.
Use this if you need a focused way to test and compare how different vision-language AI models interpret and reason about spatial relationships like 'behind,' 'left of,' or 'at the edge of' within images.
Not ideal if you're looking for a broad benchmark that evaluates general visual reasoning, object recognition, or question answering, as this dataset specifically targets spatial understanding.
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140
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12
Language
Python
License
Apache-2.0
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Last pushed
Mar 25, 2023
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0
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