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 |
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01.06.2023
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Yang, Cheng Yoshie, Osamu Lu, Hongtao Cui, Quan Wu, Hao Yi, Muyang |
| Author_xml | – sequence: 1 givenname: Muyang surname: Yi fullname: Yi, Muyang organization: AI Institute, Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence,Department of Computer Science and Engineering – sequence: 2 givenname: Quan surname: Cui fullname: Cui, Quan organization: Waseda University – sequence: 3 givenname: Hao surname: Wu fullname: Wu, Hao organization: ByteDance Inc – sequence: 4 givenname: Cheng surname: Yang fullname: Yang, Cheng organization: ByteDance Inc – sequence: 5 givenname: Osamu surname: Yoshie fullname: Yoshie, Osamu organization: Waseda University – sequence: 6 givenname: Hongtao surname: Lu fullname: Lu, Hongtao organization: AI Institute, Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence,Department of Computer Science and Engineering |
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| Snippet | Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering... |
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| SubjectTerms | and reasoning Codes Computer vision language Location awareness Network architecture Semantic segmentation Semantics Vision Visualization |
| Title | A Simple Framework for Text-Supervised Semantic Segmentation |
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