Visual Spatial Reasoning

30 Apr 2022  ·  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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Datasets


Introduced in the Paper:

VSR

Used in the Paper:

COCO Visual Question Answering CLEVR NLVR VALSE

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Visual Reasoning VSR LXMERT accuracy 70.1 # 1
Visual Reasoning VSR CLIP (finetuned) accuracy 65.1 # 3
Visual Reasoning VSR CLIP (frozen) accuracy 56.0 # 4
Visual Reasoning VSR ViLT accuracy 69.3 # 2
Visual Reasoning VSR VisualBERT accuracy 55.2 # 5

Methods