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|a 10.1109/TPAMI.2019.2960224
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|a eng
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| 100 |
1 |
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|a Mittal, Sudhanshu
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 05.03.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level classification with limited data has only drawn attention recently. In this work, we propose an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. The proposed approach relies on adversarial training with a feature matching loss to learn from unlabeled images. It uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The dual-branch approach reduces both the low-level and the high-level artifacts typical when training with few labels. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks-PASCAL VOC 2012, PASCAL-Context, and Cityscapes-the approach achieves new state-of-the-art in semi-supervised learning
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Tatarchenko, Maxim
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Brox, Thomas
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 4 vom: 01. Apr., Seite 1369-1379
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:43
|g year:2021
|g number:4
|g day:01
|g month:04
|g pages:1369-1379
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|u http://dx.doi.org/10.1109/TPAMI.2019.2960224
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