DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Inkyu | ko |
dc.contributor.author | Kim, Dong-Jin | ko |
dc.contributor.author | Cho, Jae Won | ko |
dc.contributor.author | Woo, Sanghyun | ko |
dc.contributor.author | Park, KwanYong | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2022-11-29T02:01:15Z | - |
dc.date.available | 2022-11-29T02:01:15Z | - |
dc.date.created | 2022-11-25 | - |
dc.date.issued | 2021-10-17 | - |
dc.identifier.citation | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021, pp.8568 - 8578 | - |
dc.identifier.uri | http://hdl.handle.net/10203/301200 | - |
dc.description.abstract | Unsupervised Domain Adaptation (UDA) for semantic segmentation has been actively studied to mitigate the domain gap between label-rich source data and unlabeled target data. Despite these efforts, UDA still has a long way to go to reach the fully supervised performance. To this end, we propose a Labeling Only if Required strategy, LabOR, where we introduce a human-in-the-loop approach to adaptively give scarce labels to points that a UDA model is uncertain about. In order to find the uncertain points, we generate an inconsistency mask using the proposed adaptive pixel selector and we label these segment-based regions to achieve near supervised performance with only a small fraction (about 2.2%) ground truth points, which we call "Segment based Pixel-Labeling (SPL)." To further reduce the efforts of the human annotator, we also propose "Point based Pixel-Labeling (PPL)," which finds the most representative points for labeling within the generated inconsistency mask. This reduces efforts from 2.2% segment label -> 40 points label while minimizing performance degradation. Through extensive experimentation, we show the advantages of this new framework for domain adaptive semantic segmentation while minimizing human labor costs. | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society | - |
dc.title | LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation | - |
dc.type | Conference | - |
dc.identifier.wosid | 000798743207031 | - |
dc.identifier.scopusid | 2-s2.0-85112499673 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 8568 | - |
dc.citation.endingpage | 8578 | - |
dc.citation.publicationname | 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 | - |
dc.identifier.conferencecountry | CN | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1109/iccv48922.2021.00847 | - |
dc.contributor.localauthor | Kweon, In-So | - |
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