Papers › Visual Spatial Reasoning
Visual Spatial Reasoning
Fangyu Liu, Guy Emerson, Nigel Collier
Spatial relations are a basic part of human cognition. However, they are expressed in natural language in a variety of ways, and previous work has suggested that current vision-and-language models (VLMs) struggle to capture relational information. In this paper, we present Visual Spatial Reasoning (VSR), a dataset containing more than 10k natural text-image pairs with 66 types of spatial relations in English (such as: under, in front of, and facing). While using a seemingly simple annotation format, we show how the dataset includes challenging linguistic phenomena, such as varying reference frames. We demonstrate a large gap between human and model performance: the human ceiling is above 95%, while state-of-the-art models only achieve around 70%. We observe that VLMs' by-relation performances have little correlation with the number of training examples and the tested models are in general incapable of recognising relations concerning the orientations of objects.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Reasoning | VSR | LXMERT | accuracy | 70.1 | #1 of 5 | Archive leaderboard | report |
| Visual Reasoning | VSR | ViLT | accuracy | 69.3 | #2 of 5 | Archive leaderboard | report |
| Visual Reasoning | VSR | CLIP (finetuned) | accuracy | 65.1 | #3 of 5 | Archive leaderboard | report |
| Visual Reasoning | VSR | CLIP (frozen) | accuracy | 56.0 | #4 of 5 | Archive leaderboard | report |
| Visual Reasoning | VSR | VisualBERT | accuracy | 55.2 | #5 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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