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[2602.14788] VIPA: Visual Informative Part Attention for Referring Image Segmentation
Summary
The paper presents VIPA, a novel framework for Referring Image Segmentation that enhances attention mechanisms by leveraging informative visual contexts, outperforming existing methods on multiple benchmarks.
Why It Matters
Referring Image Segmentation is crucial for applications in computer vision, particularly in understanding and interpreting images based on natural language descriptions. The VIPA framework addresses limitations in current methods by improving semantic consistency and reducing noise, which can lead to advancements in AI's ability to process visual information accurately.
Key Takeaways
- VIPA framework utilizes Visual Informative Part Attention for improved segmentation.
- Introduces a Visual Expression Generator to enhance context comprehension.
- Demonstrates superior performance over existing state-of-the-art methods.
- Focuses on reducing noise and enhancing semantic consistency in image segmentation.
- Extensive experiments validate the effectiveness of the proposed approach.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.14788 (cs) [Submitted on 16 Feb 2026] Title:VIPA: Visual Informative Part Attention for Referring Image Segmentation Authors:Yubin Cho, Hyunwoo Yu, Kyeongbo Kong, Kyomin Sohn, Bongjoon Hyun, Suk-Ju Kang View a PDF of the paper titled VIPA: Visual Informative Part Attention for Referring Image Segmentation, by Yubin Cho and 4 other authors View PDF Abstract:Referring Image Segmentation (RIS) aims to segment a target object described by a natural language expression. Existing methods have evolved by leveraging the vision information into the language tokens. To more effectively exploit visual contexts for fine-grained segmentation, we propose a novel Visual Informative Part Attention (VIPA) framework for referring image segmentation. VIPA leverages the informative parts of visual contexts, called a visual expression, which can effectively provide the structural and semantic visual target information to the network. This design reduces high-variance cross-modal projection and enhances semantic consistency in an attention mechanism of the referring image segmentation. We also design a visual expression generator (VEG) module, which retrieves informative visual tokens via local-global linguistic context cues and refines the retrieved tokens for reducing noise information and sharing informative visual attributes. This module allows the visual expression to consider comprehensive contexts and capture semantic ...