Color Variants Identification via Contrastive Self-Supervised Representation Learning

• URL: http://arxiv.org/abs/2104.08581v1
• Date: Sat, 17 Apr 2021 15:51:56 GMT
• Title: Color Variants Identification via Contrastive Self-Supervised Representation Learning
• Authors: Ujjal Kr Dutta, Sandeep Repakula, Maulik Parmar, Abhinav Ravi
• Abstract summary: We utilize deep visual Representation Learning to address the problem of identification of color variants. We propose a novel contrastive loss based self-supervised color variants model. We evaluate our method both quantitatively and qualitatively to show that it outperforms existing self-supervised methods.
• Score: 2.3449131636069898
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we utilize deep visual Representation Learning to address the problem of identification of color variants. In particular, we address color variants identification in fashion products, which refers to the problem of identifying fashion products that match exactly in their design (or style), but only to differ in their color. Firstly, we solve this problem by obtaining manual annotations depicting whether two products are color variants. Having obtained such annotations, we train a triplet loss based neural network model to learn deep 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. Hence, we rather explore the use of self-supervised learning to obtain the representations. We observed that existing state-of-the-art self-supervised methods do not perform competitive against the supervised version of our color variants model. To address this, we additionally propose a novel contrastive loss based self-supervised color variants model. Intuitively, our model focuses on different parts of an object in a fixed manner, rather than focusing on random crops typically used for data augmentation in existing methods. We evaluate our method both quantitatively and qualitatively to show that it outperforms existing self-supervised methods, and at times, the supervised model as well.

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