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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2404.04231 |
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| _version_ | 1866910399869222912 |
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| author | Wu, Ji-Jia Chang, Andy Chia-Hao Chuang, Chieh-Yu Chen, Chun-Pei Liu, Yu-Lun Chen, Min-Hung Hu, Hou-Ning Chuang, Yung-Yu Lin, Yen-Yu |
| author_facet | Wu, Ji-Jia Chang, Andy Chia-Hao Chuang, Chieh-Yu Chen, Chun-Pei Liu, Yu-Lun Chen, Min-Hung Hu, Hou-Ning Chuang, Yung-Yu Lin, Yen-Yu |
| contents | This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations. Existing methods have demonstrated that contrastive learning on image-text pairs effectively aligns visual segments with the meanings of texts. We notice that there is a discrepancy between text alignment and semantic segmentation: A text often consists of multiple semantic concepts, whereas semantic segmentation strives to create semantically homogeneous segments. To address this issue, we propose a novel framework, Image-Text Co-Decomposition (CoDe), where the paired image and text are jointly decomposed into a set of image regions and a set of word segments, respectively, and contrastive learning is developed to enforce region-word alignment. To work with a vision-language model, we present a prompt learning mechanism that derives an extra representation to highlight an image segment or a word segment of interest, with which more effective features can be extracted from that segment. Comprehensive experimental results demonstrate that our method performs favorably against existing text-supervised semantic segmentation methods on six benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation Wu, Ji-Jia Chang, Andy Chia-Hao Chuang, Chieh-Yu Chen, Chun-Pei Liu, Yu-Lun Chen, Min-Hung Hu, Hou-Ning Chuang, Yung-Yu Lin, Yen-Yu Computer Vision and Pattern Recognition This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations. Existing methods have demonstrated that contrastive learning on image-text pairs effectively aligns visual segments with the meanings of texts. We notice that there is a discrepancy between text alignment and semantic segmentation: A text often consists of multiple semantic concepts, whereas semantic segmentation strives to create semantically homogeneous segments. To address this issue, we propose a novel framework, Image-Text Co-Decomposition (CoDe), where the paired image and text are jointly decomposed into a set of image regions and a set of word segments, respectively, and contrastive learning is developed to enforce region-word alignment. To work with a vision-language model, we present a prompt learning mechanism that derives an extra representation to highlight an image segment or a word segment of interest, with which more effective features can be extracted from that segment. Comprehensive experimental results demonstrate that our method performs favorably against existing text-supervised semantic segmentation methods on six benchmark datasets. |
| title | Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.04231 |