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.

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.