{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/prototype-learning-for-automatic-check-out","title":"Prototype Learning for Automatic Check-Out","arxiv_id":null,"date":"2023-02-22","proceeding":"IEEE Transactions on Multimedia 2023 2","authors":["Hao Chen; Xiu-Shen Wei; Liang Xiao"],"abstract":"The basic goal of Automatic Check-Out (ACO) task is to accurately predict the categories and quantities of products selected by customers in the check-out images. However, there is a significant domain gap between the single-product exemplars as training data and the check-out images as testing data. To mitigate the domain gap, we propose a novel method termed as Prototype Learning for Automatic Check-Out (PLACO). In PLACO, prototype learning is designed to reach the goal in two ways. Specifically, in the prototype-based classifier learning module, to fully exploit the invariance of category prototypes, the prototypes obtained from the single-product exemplars are employed to generate classifiers for classifying the proposals of check-out image. On the other side, in prototype alignment module, prototypes for both the single-product exemplar and check-out image domains are entered simultaneously to ensure intra-category compactness and inter-category sparsity. Moreover, to further improve the performance of PLACO, we develop a discriminative re-ranking module 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. Experiments are conducted on the large-scale RPC dataset for evaluations. Our PLACO obtains the optimal results in both traditional ACO task setting and incremental task setting.","url_abs":"https://ieeexplore.ieee.org/abstract/document/10049664","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10049664","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"prototype-learning-for-automatic-check-out","repo_url":"https://github.com/2023-MindSpore-1/ms-code-111","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"re-ranking","task_name":"Re-Ranking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}