DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Myungchul | ko |
dc.contributor.author | Woo, Sanghyun | ko |
dc.contributor.author | Kim, Dahun | ko |
dc.contributor.author | Kweon, In So | ko |
dc.date.accessioned | 2021-10-27T01:30:16Z | - |
dc.date.available | 2021-10-27T01:30:16Z | - |
dc.date.created | 2021-10-27 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.citation | IEEE Winter Conference on Applications of Computer Vision (WACV), pp.928 - 937 | - |
dc.identifier.issn | 2472-6737 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288324 | - |
dc.description.abstract | Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more efficient design than its two-stage counterparts. Besides, its global mask representation often leads to superior accuracy to the two-stage Mask R-CNN which has been dominant thus far. Despite the promising advances in single-stage methods, finer delineation of instance boundaries still remains unexcavated. Indeed, boundary information provides a strong shape representation that can operate in synergy with the fully-convolutional mask features of the single-stage segmenter. In this work, we propose Boundary Basis based Instance Segmentation(B2Inst) to learn a global boundary representation that can complement existing global-mask-based methods that are often lacking high-frequency details. Besides, we devise a unified quality measure of both mask and boundary and introduce a network block that learns to score the per-instance predictions of itself. When applied to the strongest baselines in single-stage instance segmentation, our B2Inst leads to consistent improvements and accurately parse out the instance boundaries in a scene. Regardless of being single-stage or two-stage frameworks, we outperform the existing state-of-the-art methods on the COCO dataset with the same ResNet-50 and ResNet-101 backbones. | - |
dc.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation | - |
dc.type | Conference | - |
dc.identifier.wosid | 000692171000092 | - |
dc.identifier.scopusid | 2-s2.0-85106897615 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 928 | - |
dc.citation.endingpage | 937 | - |
dc.citation.publicationname | IEEE Winter Conference on Applications of Computer Vision (WACV) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | ELECTR NETWORK | - |
dc.identifier.doi | 10.1109/WACV48630.2021.00097 | - |
dc.contributor.localauthor | Kweon, In So | - |
dc.contributor.nonIdAuthor | Kim, Myungchul | - |
dc.contributor.nonIdAuthor | Woo, Sanghyun | - |
dc.contributor.nonIdAuthor | Kim, Dahun | - |
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