Abstract
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 (e.g., under, in front of, 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.1- Anthology ID:
- 2023.tacl-1.37
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 11
- Month:
- Year:
- 2023
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 635–651
- Language:
- URL:
- https://aclanthology.org/2023.tacl-1.37
- DOI:
- 10.1162/tacl_a_00566
- Cite (ACL):
- Fangyu Liu, Guy Emerson, and Nigel Collier. 2023. Visual Spatial Reasoning. Transactions of the Association for Computational Linguistics, 11:635–651.
- Cite (Informal):
- Visual Spatial Reasoning (Liu et al., TACL 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.tacl-1.37.pdf