A Simple Framework for Text-Supervised Semantic Segmentation

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pretraining (CL...

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Veröffentlicht in:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) S. 7071 - 7080
Hauptverfasser: Yi, Muyang, Cui, Quan, Wu, Hao, Yang, Cheng, Yoshie, Osamu, Lu, Hongtao
Format: Tagungsbericht
Sprache:Englisch
Japanisch
Veröffentlicht: IEEE 01.06.2023
Schlagworte:
and reasoning
Codes
Computer vision
language
Location awareness
Network architecture
Semantic segmentation
Semantics
Vision
Visualization
ISSN:1063-6919
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Zusammenfassung:Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pretraining (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly amelio-rate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at github.com/muyangyi/SimSeg.
ISSN:1063-6919
DOI:10.1109/CVPR52729.2023.00683