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|a 10.1109/TIP.2022.3163527
|2 doi
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|a eng
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| 100 |
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|a Chen, Hao
|e verfasserin
|4 aut
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|a Self-Supervised Multi-Category Counting Networks for Automatic Check-Out
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|c 2022
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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 Revised 12.04.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The practical task of Automatic Check-Out (ACO) is to accurately predict the presence and count of each product in an arbitrary product combination. Beyond the large-scale and the fine-grained nature of product categories as its main challenges, products are always continuously updated in realistic check-out scenarios, which is also required to be solved in an ACO system. Previous work in this research line almost depends on the supervisions of labor-intensive bounding boxes of products by performing a detection paradigm. While, in this paper, we propose a Self-Supervised Multi-Category Counting (S2MC2) network to leverage the point-level supervisions of products in check-out images to both lower the labeling cost and be able to return ACO predictions in a class incremental setting. Specifically, as a backbone, our S2MC2 is built upon a counting module in a class-agnostic counting fashion. Also, it consists of several crucial components including an attention module for capturing fine-grained patterns and a domain adaptation module for reducing the domain gap between single product images as training and check-out images as test. Furthermore, a self-supervised approach is utilized in S2MC2 to initialize the parameters of its backbone for better performance. By conducting comprehensive experiments on the large-scale automatic check-out dataset RPC, we demonstrate that our proposed S2MC2 achieves superior accuracy in both traditional and incremental settings of ACO tasks over the competing baselines
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|a Journal Article
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|a Zhou, Yangzhun
|e verfasserin
|4 aut
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| 700 |
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|a Li, Jun
|e verfasserin
|4 aut
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| 700 |
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|a Wei, Xiu-Shen
|e verfasserin
|4 aut
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| 700 |
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|a Xiao, Liang
|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 31(2022) vom: 05., Seite 3004-3016
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|x 1941-0042
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|g volume:31
|g year:2022
|g day:05
|g pages:3004-3016
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|u http://dx.doi.org/10.1109/TIP.2022.3163527
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