LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385335" target="_blank" >RIV/68407700:21230/25:00385335 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR52734.2025.00915" target="_blank" >https://doi.org/10.1109/CVPR52734.2025.00915</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52734.2025.00915" target="_blank" >10.1109/CVPR52734.2025.00915</a>
Alternative languages
Result language
angličtina
Original language name
LPOSS: Label Propagation Over Patches and Pixels for Open-vocabulary Semantic Segmentation
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GM21-28830M" target="_blank" >GM21-28830M: Learning Universal Visual Representation with Limited Supervision</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3315-4364-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
9794-9803
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Nashville
Event date
Jun 11, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001601106700332