Boosting Unsupervised Segmentation Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929608121647104 |
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| author | Sari, Alp Eren Locatello, Francesco Favaro, Paolo |
| author_facet | Sari, Alp Eren Locatello, Francesco Favaro, Paolo |
| contents | We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoiding substantial computational costs. Secondly, we introduce a multi-scale consistency criterion, based on a teacher-student training scheme. This criterion matches segmentation masks predicted from regions of the input image extracted at different resolutions to each other. Experimental results on several benchmarks used in unsupervised segmentation learning demonstrate the effectiveness of our proposed techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03392 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Unsupervised Segmentation Learning Sari, Alp Eren Locatello, Francesco Favaro, Paolo Computer Vision and Pattern Recognition We present two practical improvement techniques for unsupervised segmentation learning. These techniques address limitations in the resolution and accuracy of predicted segmentation maps of recent state-of-the-art methods. Firstly, we leverage image post-processing techniques such as guided filtering to refine the output masks, improving accuracy while avoiding substantial computational costs. Secondly, we introduce a multi-scale consistency criterion, based on a teacher-student training scheme. This criterion matches segmentation masks predicted from regions of the input image extracted at different resolutions to each other. Experimental results on several benchmarks used in unsupervised segmentation learning demonstrate the effectiveness of our proposed techniques. |
| title | Boosting Unsupervised Segmentation Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.03392 |