Metric Learning for Semantic-based Clothes Retrieval
Release Date：2022-03-16 Author：YANG Bo, GUO Caili, LI Zheng Click：
Metric Learning for Semantic-based Clothes Retrieval
YANG Bo1, GUO Caili1, 2, LI Zheng1
(1 Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2 Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China)
Abstract: Existing clothes retrieval methods mostly adopt the binary supervision in metric learning. For each iteration, only clothes belonging to the same instance are positive samples, and all other clothes are “indistinguishable” negative samples, which causes the following problem. The relevance between query and candidates is only treated as relevant or irrelevant, which makes the model difficult to learn the continuous semantic similarities between clothes. Clothes that do not belong to the same instance are completely considered irrelevant and are uniformly pushed away from the query by an equal margin in the embedding space, which is not consistent with the ideal retrieval results. Motivated by this, we propose a novel method called semantic-based clothes retrieval (SCR). In SCR, we measure the semantic similarities between clothes and design a new adaptive loss based on these similarities. The margin in the proposed adaptive loss can vary with different semantic similarities between the anchor and negative samples. In this way, more coherent embedding space can be learned, where candidates with higher semantic similarities are mapped closer to the query than those with lower ones. We use Recall@K and normalized Discounted Cumulative Gain (nDCG) as evaluation metrics to conduct experiments on the DeepFashion dataset and have achieved better performance.
Keywords: clothes retrieval; metric learning; semantic-based retrieval
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