Soft labelling for semantic segmentation: Bringing coherence to label down-sampling
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913236639547392 |
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| author | Alcover-Couso, Roberto Escudero-Vinolo, Marcos SanMiguel, Juan C. Martinez, Jose M. |
| author_facet | Alcover-Couso, Roberto Escudero-Vinolo, Marcos SanMiguel, Juan C. Martinez, Jose M. |
| contents | In semantic segmentation, training data down-sampling is commonly performed due to limited resources, the need to adapt image size to the model input, or improve data augmentation. This down-sampling typically employs different strategies for the image data and the annotated labels. Such discrepancy leads to mismatches between the down-sampled color and label images. Hence, the training performance significantly decreases as the down-sampling factor increases. In this paper, we bring together the down-sampling strategies for the image data and the training labels. To that aim, we propose a novel framework for label down-sampling via soft-labeling that better conserves label information after down-sampling. Therefore, fully aligning soft-labels with image data to keep the distribution of the sampled pixels. This proposal also produces reliable annotations for under-represented semantic classes. Altogether, it allows training competitive models at lower resolutions. Experiments show that the proposal outperforms other down-sampling strategies. Moreover, state-of-the-art performance is achieved for reference benchmarks, but employing significantly less computational resources than foremost approaches. This proposal enables competitive research for semantic segmentation under resource constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_13961 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Soft labelling for semantic segmentation: Bringing coherence to label down-sampling Alcover-Couso, Roberto Escudero-Vinolo, Marcos SanMiguel, Juan C. Martinez, Jose M. Computer Vision and Pattern Recognition In semantic segmentation, training data down-sampling is commonly performed due to limited resources, the need to adapt image size to the model input, or improve data augmentation. This down-sampling typically employs different strategies for the image data and the annotated labels. Such discrepancy leads to mismatches between the down-sampled color and label images. Hence, the training performance significantly decreases as the down-sampling factor increases. In this paper, we bring together the down-sampling strategies for the image data and the training labels. To that aim, we propose a novel framework for label down-sampling via soft-labeling that better conserves label information after down-sampling. Therefore, fully aligning soft-labels with image data to keep the distribution of the sampled pixels. This proposal also produces reliable annotations for under-represented semantic classes. Altogether, it allows training competitive models at lower resolutions. Experiments show that the proposal outperforms other down-sampling strategies. Moreover, state-of-the-art performance is achieved for reference benchmarks, but employing significantly less computational resources than foremost approaches. This proposal enables competitive research for semantic segmentation under resource constraints. |
| title | Soft labelling for semantic segmentation: Bringing coherence to label down-sampling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2302.13961 |