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arXiv · 2010.00515

Linguistic Structure Guided Context Modeling for Referring Image Segmentation

Tianrui Hui·Si Liu·Shaofei Huang·Guanbin Li·Sansi Yu·Faxi Zhang·Jizhong Han
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Abstract

Referring image segmentation aims to predict the foreground mask of the object referred by a natural language sentence. Multimodal context of the sentence is crucial to distinguish the referent from the background. Existing methods either insufficiently or redundantly model the multimodal context. To tackle this problem, we propose a "gather-propagate-distribute" scheme to model multimodal context by cross-modal interaction and implement this scheme as a novel Linguistic Structure guided Context Modeling (LSCM) module. Our LSCM module builds a Dependency Parsing Tree suppressed Word Graph (DPT-WG) which guides all the words to include valid multimodal context of the sentence while excluding disturbing ones through three steps over the multimodal feature, i.e., gathering, constrained propagation and distributing. Extensive experiments on four benchmarks demonstrate that our method outperforms all the previous state-of-the-arts.

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Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, Jizhong Han. 2020-10-05. Linguistic Structure Guided Context Modeling for Referring Image Segmentation. https://arxiv.org/abs/2010.00515

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