RISAM: Referring Image Segmentation via Mutual-Aware Attention Features

• URL: http://arxiv.org/abs/2311.15727v4
• Date: Tue, 21 May 2024 10:06:33 GMT
• Title: RISAM: Referring Image Segmentation via Mutual-Aware Attention Features
• Authors: Mengxi Zhang, Yiming Liu, Xiangjun Yin, Huanjing Yue, Jingyu Yang,
• Abstract summary: Referring image segmentation (RIS) aims to segment a particular region based on a language expression prompt. Existing methods incorporate linguistic features into visual features and obtain multi-modal features for mask decoding. We propose MARIS, a referring image segmentation method that leverages the Segment Anything Model (SAM) and introduces a mutual-aware attention mechanism.
• Score: 13.64992652002458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring image segmentation (RIS) aims to segment a particular region based on a language expression prompt. Existing methods incorporate linguistic features into visual features and obtain multi-modal features for mask decoding. However, these methods may segment the visually salient entity instead of the correct referring region, as the multi-modal features are dominated by the abundant visual context. In this paper, we propose MARIS, a referring image segmentation method that leverages the Segment Anything Model (SAM) and introduces a mutual-aware attention mechanism to enhance the cross-modal fusion via two parallel branches. Specifically, our mutual-aware attention mechanism consists of Vision-Guided Attention and Language-Guided Attention, which bidirectionally model the relationship between visual and linguistic features. Correspondingly, we design a Mask Decoder to enable explicit linguistic guidance for more consistent segmentation with the language expression. To this end, a multi-modal query token is proposed to integrate linguistic information and interact with visual information simultaneously. Extensive experiments on three benchmark datasets show that our method outperforms the state-of-the-art RIS methods. Our code will be publicly available.

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