DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2505.11676v1
• Date: Fri, 16 May 2025 20:25:42 GMT
• Title: DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation
• Authors: Ziyu Zhao, Xiaoguang Li, Linjia Shi, Nasrin Imanpour, Song Wang,
• Abstract summary: Open-vocabulary semantic segmentation aims to segment images into distinct semantic regions at the pixel level.<n>Current methods utilize text embeddings from pre-trained vision-language models like CLIP.<n>We propose a dual prompting framework, DPSeg, for this task.
• Score: 16.64056234334767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary semantic segmentation aims to segment images into distinct semantic regions for both seen and unseen categories at the pixel level. Current methods utilize text embeddings from pre-trained vision-language models like CLIP but struggle with the inherent domain gap between image and text embeddings, even after extensive alignment during training. Additionally, relying solely on deep text-aligned features limits shallow-level feature guidance, which is crucial for detecting small objects and fine details, ultimately reducing segmentation accuracy. To address these limitations, we propose a dual prompting framework, DPSeg, for this task. Our approach combines dual-prompt cost volume generation, a cost volume-guided decoder, and a semantic-guided prompt refinement strategy that leverages our dual prompting scheme to mitigate alignment issues in visual prompt generation. By incorporating visual embeddings from a visual prompt encoder, our approach reduces the domain gap between text and image embeddings while providing multi-level guidance through shallow features. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches on multiple public datasets.

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