Learning to Adapt Structured Output Space for Semantic Segmentation
Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to t...
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| Vydané v: | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition s. 7472 - 7481 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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IEEE
01.06.2018
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| ISSN: | 1063-6919 |
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| Abstract | Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality. |
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| AbstractList | Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. As the labeling process is tedious and labor intensive, developing algorithms that can adapt source ground truth labels to the target domain is of great interest. In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation. Considering semantic segmentations as structured outputs that contain spatial similarities between the source and target domains, we adopt adversarial learning in the output space. To further enhance the adapted model, we construct a multi-level adversarial network to effectively perform output space domain adaptation at different feature levels. Extensive experiments and ablation study are conducted under various domain adaptation settings, including synthetic-to-real and cross-city scenarios. We show that the proposed method performs favorably against the state-of-the-art methods in terms of accuracy and visual quality. |
| Author | Yang, Ming-Hsuan Hung, Wei-Chih Chandraker, Manmohan Sohn, Kihyuk Schulter, Samuel Tsai, Yi-Hsuan |
| Author_xml | – sequence: 1 givenname: Yi-Hsuan surname: Tsai fullname: Tsai, Yi-Hsuan – sequence: 2 givenname: Wei-Chih surname: Hung fullname: Hung, Wei-Chih – sequence: 3 givenname: Samuel surname: Schulter fullname: Schulter, Samuel – sequence: 4 givenname: Kihyuk surname: Sohn fullname: Sohn, Kihyuk – sequence: 5 givenname: Ming-Hsuan surname: Yang fullname: Yang, Ming-Hsuan – sequence: 6 givenname: Manmohan surname: Chandraker fullname: Chandraker, Manmohan |
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| Snippet | Convolutional neural network-based approaches for semantic segmentation rely on supervision with pixel-level ground truth, but may not generalize well to... |
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| SubjectTerms | Adaptation models Image segmentation Layout Prediction algorithms Semantics Task analysis Training |
| Title | Learning to Adapt Structured Output Space for Semantic Segmentation |
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