DiSa: Saliency-Aware Foreground-Background Disentangled Framework for Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2601.20064v1
• Date: Tue, 27 Jan 2026 21:15:10 GMT
• Title: DiSa: Saliency-Aware Foreground-Background Disentangled Framework for Open-Vocabulary Semantic Segmentation
• Authors: Zhen Yao, Xin Li, Taotao Jing, Shuai Zhang, Mooi Choo Chuah,
• Abstract summary: Open-vocabulary semantic segmentation aims to assign labels to every pixel in an image based on text labels.<n>Existing approaches typically utilize vision-language models (VLMs), such as CLIP, for dense prediction.<n>We introduce DiSa, a novel saliency-aware foreground-background disentangled framework.
• Score: 16.57245702815661
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Open-vocabulary semantic segmentation aims to assign labels to every pixel in an image based on text labels. Existing approaches typically utilize vision-language models (VLMs), such as CLIP, for dense prediction. However, VLMs, pre-trained on image-text pairs, are biased toward salient, object-centric regions and exhibit two critical limitations when adapted to segmentation: (i) Foreground Bias, which tends to ignore background regions, and (ii) Limited Spatial Localization, resulting in blurred object boundaries. To address these limitations, we introduce DiSa, a novel saliency-aware foreground-background disentangled framework. By explicitly incorporating saliency cues in our designed Saliency-aware Disentanglement Module (SDM), DiSa separately models foreground and background ensemble features in a divide-and-conquer manner. Additionally, we propose a Hierarchical Refinement Module (HRM) that leverages pixel-wise spatial contexts and enables channel-wise feature refinement through multi-level updates. Extensive experiments on six benchmarks demonstrate that DiSa consistently outperforms state-of-the-art methods.

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