Large-scale product recognition is one of the major applications of computer
vision and machine learning in the e-commerce domain. Since the number of
products is typically much larger than the number of categories of products,
image-based product recognition is often cast as a visual search rather than a
classification problem. It is also one of the instances of super fine-grained
recognition, where there are many products with slight or subtle visual
differences. It has always been a challenge to create a benchmark dataset for
training and evaluation on various visual search solutions in a real-world
setting. This motivated creation of eProduct, a dataset consisting of 2.5
million product images towards accelerating development in the areas of
self-supervised learning, weakly-supervised learning, and multimodal learning,
for fine-grained recognition. We present eProduct as a training set and an
evaluation set, where the training set contains 1.3M+ listing images with
titles and hierarchical category labels, for model development, and the
evaluation set includes 10,000 query and 1.1 million index images for visual
search evaluation. We will present eProduct's construction steps, provide
analysis about its diversity and cover the performance of baseline models
trained on it.

本文介绍了 eProduct 数据集的创建，该数据集包含 250 万个产品图片。eProduct 可作为一个训练集和评估集来加速自监督学习、弱监督学习和多模态学习等领域的发展，特别是针对细粒度识别问题，如视觉搜索。通过基于此数据集训练的基线模型的性能，分析了 eProduct 数据集的多样性和使用结果。

eProduct: 应对产品识别挑战的百万级视觉搜索基准

eProduct: A Million-Scale Visual Search Benchmark to Address Product  Recognition Challenges

With the rapid development of electronic commerce, the way of shopping has
experienced a revolutionary evolution. To fully meet customers' massive and
diverse online shopping needs with quick response, the retailing AI system
needs to automatically recognize products from images and videos at the
stock-keeping unit (SKU) level with high accuracy. However, product recognition
is still a challenging task, since many of SKU-level products are fine-grained
and visually similar by a rough glimpse. Although there are already some
products benchmarks available, these datasets are either too small (limited
number of products) or noisy-labeled (lack of human labeling). In this paper,
we construct a human-labeled product image dataset named "Products-10K", which
contains 10,000 fine-grained SKU-level products frequently bought by online
customers in JD.com. Based on our new database, we also introduced several
useful tips and tricks for fine-grained product recognition. The products-10K
dataset is available via this https URL

电子商务日益发展，零售 AI 系统需要自动识别高准确度的 SKU 级别的产品，本研究构建了一个包含 10,000 个人工标注细粒度 SKU 级别产品的图像数据集，并针对细粒度产品识别提出了多种有用的技巧。

Products-10K：一项大规模产品识别数据集

Products-10K: A Large-scale Product Recognition Dataset

Assistive solutions for a better shopping experience can improve the quality
of life of people, in particular also of visually impaired shoppers. We present
a system that visually recognizes the fine-grained product classes of items on
a shopping list, in shelves images taken with a smartphone in a grocery store.
Our system consists of three components: (a) We automatically recognize useful
text on product packaging, e.g., product name and brand, and build a mapping of
words to product classes based on the large-scale GroceryProducts dataset. When
the user populates the shopping list, we automatically infer the product class
of each entered word. (b) We perform fine-grained product class recognition
when the user is facing a shelf. We discover discriminative patches on product
packaging to differentiate between visually similar product classes and to
increase the robustness against continuous changes in product design. (c) We
continuously improve the recognition accuracy through active learning. Our
experiments show the robustness of the proposed method against cross-domain
challenges, and the scalability to an increasing number of products with
minimal re-training.

介绍了一种辅助购物系统，该系统可视化识别购物者所需产品，并通过主动学习进一步提高识别准确性，为视障人士等提供更好的购物体验。