Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models
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With the advances in large scale vision-and-language models (VLMs) it is of interest to assess their performance on various visual reasoning tasks such as counting, referring expressions and general visual question answering. The focus of this work is to study the ability of these models to understanding spatial relations. Previously, this has been tackled using image-text matching (Liu, Emerson, and Collier 2022) or visual question answering task, both showing poor performance and a large gap compared to human performance. To better understand the gap, we present fine-grained compositional grounding of spatial relationships and propose a bottom up approach for ranking spatial clauses and evaluating the performance of spatial relationship reasoning task. We propose to combine the evidence from grounding noun phrases corresponding to objects and their locations to compute the final rank of the spatial clause. We demonstrate the approach on representative vision-language models (Tan and Bansal 2019; Gupta et al. 2022; Kamath et al. 2021) and compare and highlight their abilities to reason about spatial relationships.
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