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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 277–293Cite as

Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13685)

Abstract

Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images). To mitigate the gap, we propose a method, termed as PSP, to perform Prototype-based classifier learning from Single-Product exemplars. In PSP, by revealing the advantages of representing category semantics, the prototype representation of each product category is firstly obtained from single-product exemplars. Based on the prototypes, it then generates categorical classifiers with a background classifier to not only recognize fine-grained product categories but also distinguish background upon product proposals derived from check-out images. To further improve the ACO accuracy, we develop discriminative re-ranking to both adjust the predicted scores of product proposals for bringing more discriminative ability in classifier learning and provide a reasonable sorting possibility by considering the fine-grained nature. Moreover, a multi-label recognition loss is also equipped for modeling co-occurrence of products in check-out images. Experiments are conducted on the large-scale RPC dataset for evaluations. Our ACO result achieves 86.69%, by 6.18% improvements over state-of-the-arts, which demonstrates the superiority of PSP. Our codes are available at https://github.com/Hao-Chen-NJUST/PSP.

Keywords

  • Automatic check-out
  • Prototype
  • Classifier learning

X.-S. Wei and Y. Shen are also with Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, Nanjing University of Science and Technology, China. This work is supported by National Key R &D Program of China (2021YFA1001100), Natural Science Foundation of China under Grant (61871226), Natural Science Foundation of Jiangsu Province of China under Grant (BK20210340), the Fundamental Research Funds for the Central Universities (No. 30920041111, No. NJ2022028), CAAI-Huawei MindSpore Open Fund, Beijing Academy of Artificial Intelligence (BAAI), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_0464).

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Acknowledgments

The authors would like to thank the anonymous reviewers for their critical and constructive comments and suggestions. We gratefully acknowledge the support of MindSpore, CANN (Compute Architecture for Neural Networks) and Ascend AI Processor used for this research.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Hao Chen, Xiu-Shen Wei, Yang Shen & Liang Xiao

  2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China

    Hao Chen & Xiu-Shen Wei

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xiu-Shen Wei

  4. Qingdao AInnovation Technology Group Co., Ltd, Qingdao, China

    Faen Zhang & Hui Xu

Authors
  1. Hao Chen
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  2. Xiu-Shen Wei
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  4. Yang Shen
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Corresponding authors

Correspondence to Xiu-Shen Wei or Liang Xiao .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Chen, H., Wei, XS., Zhang, F., Shen, Y., Xu, H., Xiao, L. (2022). Automatic Check-Out via Prototype-Based Classifier Learning from Single-Product Exemplars. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13685. Springer, Cham. https://doi.org/10.1007/978-3-031-19806-9_16

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