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

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:this https URL

@article{stojnić2025_2503.19777,
  title={ LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation },
  author={ Vladan Stojnić and Yannis Kalantidis and Jiří Matas and Giorgos Tolias },
  journal={arXiv preprint arXiv:2503.19777},
  year={ 2025 }
}