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|a 10.1109/TIP.2021.3078079
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|a eng
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| 100 |
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|a Liu, Chang
|e verfasserin
|4 aut
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|a Adaptive Linear Span Network for Object Skeleton Detection
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|c 2021
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 21.05.2021
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|a Date Revised 21.05.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Conventional networks for object skeleton detection are usually hand-crafted. Despite the effectiveness, hand-crafted network architectures lack the theoretical basis and require intensive prior knowledge to implement representation complementarity for objects/parts in different granularity. In this paper, we propose an adaptive linear span network (AdaLSN), driven by neural architecture search (NAS), to automatically configure and integrate scale-aware features for object skeleton detection. AdaLSN is formulated with the theory of linear span, which provides one of the earliest explanations for multi-scale deep feature fusion. AdaLSN is materialized by defining a mixed unit-pyramid search space, which goes beyond many existing search spaces using unit-level or pyramid-level features. Within the mixed space, we apply genetic architecture search to jointly optimize unit-level operations and pyramid-level connections for adaptive feature space expansion. AdaLSN substantiates its versatility by achieving significantly higher accuracy and latency trade-off compared with the state-of-the-arts. It also demonstrates general applicability to image-to-mask tasks such as edge detection and road extraction. Code is available at https://github.com/sunsmarterjie/SDL-Skeletongithub.com/sunsmarterjie/SDL-Skeleton
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|a Journal Article
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| 700 |
1 |
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|a Tian, Yunjie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Zhiwen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Jiao, Jianbin
|e verfasserin
|4 aut
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| 700 |
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|a Ye, Qixiang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 24., Seite 5096-5108
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|g day:24
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|u http://dx.doi.org/10.1109/TIP.2021.3078079
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