Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning

TL;DR

This paper introduces a novel self-supervised learning approach for identifying color variants in fashion e-commerce, outperforming existing SSL methods and sometimes even supervised models, especially on large-scale datasets.

Contribution

The paper proposes a new SSL-based model that focuses on different apparel parts to improve color variant identification, addressing limitations of existing SSL methods.

Findings

Outperforms existing SSL methods in accuracy.

Sometimes surpasses supervised triplet loss models.

Effective on large-scale industrial datasets.

Abstract

In this paper, we utilize deep visual Representation Learning to address an important problem in fashion e-commerce: color variants identification, i.e., identifying fashion products that match exactly in their design (or style), but only to differ in their color. At first we attempt to tackle the problem by obtaining manual annotations (depicting whether two products are color variants), and train a supervised triplet loss based neural network model to learn representations of fashion products. However, for large scale real-world industrial datasets such as addressed in our paper, it is infeasible to obtain annotations for the entire dataset, while capturing all the difficult corner cases. Interestingly, we observed that color variants are essentially manifestations of color jitter based augmentations. Thus, we instead explore Self-Supervised Learning (SSL) to solve this problem. We observed that existing state-of-the-art SSL methods perform poor, for our problem. To address this, we propose a novel SSL based color variants model that simultaneously focuses on different parts of an apparel. Quantitative and qualitative evaluation shows that our method outperforms existing SSL methods, and at times, the supervised model.