Large-scale weakly supervised product retrieval is a practically useful yet
computationally challenging problem. This paper introduces a novel solution for
the eBay Visual Search Challenge (eProduct) held at the Ninth Workshop on
Fine-Grained Visual Categorisation workshop (FGVC9) of CVPR 2022. This
competition presents two challenges: (a) E-commerce is a drastically
fine-grained domain including many products with subtle visual differences; (b)
A lacking of target instance-level labels for model training, with only coarse
category labels and product titles available. To overcome these obstacles, we
formulate a strong solution by a set of dedicated designs: (a) Instead of using
text training data directly, we mine thousands of pseudo-attributes from
product titles and use them as the ground truths for multi-label
classification. (b) We incorporate several strong backbones with advanced
training recipes for more discriminative representation learning. (c) We
further introduce a number of post-processing techniques including whitening,
re-ranking and model ensemble for retrieval enhancement. By achieving 71.53%
MAR, our solution "Involution King" achieves the second position on the
leaderboard.

本文介绍了一种用于 eBay 可视搜索挑战的弱监督商品检索的新型解决方案，该方案通过使用来自商品标题的伪属性作为多标签分类的真实值，采用多个强大的 backbones，包括白化，重排序和模型集成等后处理技术，实现了 71.53% 的 MAR，为 “Involution King” 在排行榜上获得第二名。