DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kweon, In So | - |
dc.contributor.advisor | 권인소 | - |
dc.contributor.author | Kim, Myungchul | - |
dc.date.accessioned | 2022-04-27T19:30:48Z | - |
dc.date.available | 2022-04-27T19:30:48Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=963421&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/295926 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2021.8,[v, 28 p. :] | - |
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 | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Instance segmentation▼aSingle-stage instance segmentation▼aBasis based method▼aBoundary representation | - |
dc.subject | 인스턴스 분할▼a단일단계 인스턴스 분할▼a기저 기반 인스턴스 방법론▼a경계 표현방법론 | - |
dc.title | Exploiting boundary representation for object recognition | - |
dc.title.alternative | 물체 인식을 위한 경계 표현 방법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 김명철 | - |
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