2021/04/17 by Ujjal Kr Dutta, Dutta, Ujjal Kr, Sandeep Repakula +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · Psychology · #Artificial intelligence #Clothing #Color Science and Applications #Color perception and design #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #FOS: Computer and information sciences #Feature learning #Generative Adversarial Networks and Image Synthesis #Identification (biology) #Image Enhancement Techniques #Machine Learning (cs.LG) #Machine learning #Natural language processing #Pattern recognition (psychology) #Representation (politics) #cs.CV #cs.LG #melanin and skin pigmentation
paper · pdf · doi:10.48550/arxiv.2104.08581
published in arXiv (Cornell University) (Cornell University) · Accepted In IJCAI-21 Weakly Supervised Representation Learning (WSRL) workshop
openalex publication_date 2021/04/17 · arxiv created 2021/06/30 · arxiv updated 2021/07/02 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/06
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.