LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation

• URL: http://arxiv.org/abs/2503.19777v1
• Date: Tue, 25 Mar 2025 15:47:13 GMT
• Title: LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation
• Authors: Vladan Stojnić, Yannis Kalantidis, Jiří Matas, Giorgos Tolias,
• Abstract summary: We propose a training-free method for semantic segmentation using Vision-and-Language Models (VLMs)<n>Our approach enhances the initial per-patch predictions of VLMs through label propagation.<n>Our method, called LPOSS+, performs inference over the entire image, avoiding window-based processing.
• Score: 16.021683473678515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a training-free method for open-vocabulary semantic segmentation using Vision-and-Language Models (VLMs). Our approach enhances the initial per-patch predictions of VLMs through label propagation, which jointly optimizes predictions by incorporating patch-to-patch relationships. Since VLMs are primarily optimized for cross-modal alignment and not for intra-modal similarity, we use a Vision Model (VM) that is observed to better capture these relationships. We address resolution limitations inherent to patch-based encoders by applying label propagation at the pixel level as a refinement step, significantly improving segmentation accuracy near class boundaries. Our method, called LPOSS+, performs inference over the entire image, avoiding window-based processing and thereby capturing contextual interactions across the full image. LPOSS+ achieves state-of-the-art performance among training-free methods, across a diverse set of datasets. Code: https://github.com/vladan-stojnic/LPOSS

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