Visual Spatial Reasoning

Code

• github.com/cambridgeltl/visual-spatial-reasoning
  OfficialIn paperpytorch★ 140
• github.com/sohojoe/clip_visual-spatial-reasoning
  OfficialIn paperpytorch★ 9
• github.com/ziyan-xiaoyu/spatialmqa
  pytorch★ 20
• github.com/MindCode-4/code-1/tree/main/vilt
  mindspore★ 0

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 (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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Benchmark Results

Reproductions