mlsanityData to 7 Oct 2026

Grounded Image Text Matching with Mismatched Relation Reasoning

arXiv 2308.01236 ↗First posted 2 Aug 2023cs.CV cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 55 of its 67 references that are in the map. First posted on arXiv 2 Aug 2023.

Builds on

55 of 67 references.

References · 55

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 1.

8 papers.