Improving Vision-and-Language Reasoning via Spatial Relations Modeling

• URL: http://arxiv.org/abs/2311.05298v1
• Date: Thu, 9 Nov 2023 11:54:55 GMT
• Title: Improving Vision-and-Language Reasoning via Spatial Relations Modeling
• Authors: Cheng Yang, Rui Xu, Ye Guo, Peixiang Huang, Yiru Chen, Wenkui Ding, Zhongyuan Wang, Hong Zhou
• Abstract summary: Visual commonsense reasoning (VCR) is a challenging multi-modal task. The proposed method can guide the representations to maintain more spatial context. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR.
• Score: 30.477235227733928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual commonsense reasoning (VCR) is a challenging multi-modal task, which requires high-level cognition and commonsense reasoning ability about the real world. In recent years, large-scale pre-training approaches have been developed and promoted the state-of-the-art performance of VCR. However, the existing approaches almost employ the BERT-like objectives to learn multi-modal representations. These objectives motivated from the text-domain are insufficient for the excavation on the complex scenario of visual modality. Most importantly, the spatial distribution of the visual objects is basically neglected. To address the above issue, we propose to construct the spatial relation graph based on the given visual scenario. Further, we design two pre-training tasks named object position regression (OPR) and spatial relation classification (SRC) to learn to reconstruct the spatial relation graph respectively. Quantitative analysis suggests that the proposed method can guide the representations to maintain more spatial context and facilitate the attention on the essential visual regions for reasoning. We achieve the state-of-the-art results on VCR and two other vision-and-language reasoning tasks VQA, and NLVR.

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