Grounded Image Text Matching with Mismatched Relation Reasoning

• URL: http://arxiv.org/abs/2308.01236v2
• Date: Fri, 4 Aug 2023 17:51:57 GMT
• Title: Grounded Image Text Matching with Mismatched Relation Reasoning
• Authors: Yu Wu, Yana Wei, Haozhe Wang, Yongfei Liu, Sibei Yang, Xuming He
• Abstract summary: Grounded Image Text Matching with Mismatched Relation (GITM-MR) is a novel visual-linguistic joint task. GITM-MR requires a model to first determine if an expression describes an image, then localize referred objects or ground the mismatched parts of the text. We propose the Relation-sensitive Correspondence Reasoning Network (RCRN), which incorporates relation-aware reasoning via bi-directional message propagation.
• Score: 39.524420144738684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces Grounded Image Text Matching with Mismatched Relation (GITM-MR), a novel visual-linguistic joint task that evaluates the relation understanding capabilities of transformer-based pre-trained models. GITM-MR requires a model to first determine if an expression describes an image, then localize referred objects or ground the mismatched parts of the text. We provide a benchmark for evaluating pre-trained models on this task, with a focus on the challenging settings of limited data and out-of-distribution sentence lengths. Our evaluation demonstrates that pre-trained models lack data efficiency and length generalization ability. To address this, we propose the Relation-sensitive Correspondence Reasoning Network (RCRN), which incorporates relation-aware reasoning via bi-directional message propagation guided by language structure. RCRN can be interpreted as a modular program and delivers strong performance in both length generalization and data efficiency.

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