Large-Scale Product Retrieval with Weakly Supervised Representation Learning

TL;DR

This paper presents a novel weakly supervised learning approach for large-scale product retrieval in e-commerce, leveraging pseudo-attributes, advanced backbones, and post-processing to improve accuracy.

Contribution

It introduces a comprehensive method combining pseudo-attribute mining, strong backbone models, and post-processing techniques for improved weakly supervised product retrieval.

Findings

Achieved 71.53% MAR on eBay Visual Search Challenge

Secured second place on the leaderboard

Demonstrated effectiveness of pseudo-attributes and ensemble methods

Abstract

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.