Hierarchical Proxy-based Loss for Deep Metric Learning

• URL: http://arxiv.org/abs/2103.13538v1
• Date: Thu, 25 Mar 2021 00:38:33 GMT
• Title: Hierarchical Proxy-based Loss for Deep Metric Learning
• Authors: Zhibo Yang, Muhammet Bastan, Xinliang Zhu, Doug Gray, Dimitris Samaras
• Abstract summary: Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. We present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies. Results demonstrate that our hierarchical proxy-based loss framework improves the performance of existing proxy-based losses.
• Score: 32.10423536428467
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. However, existing proxy-based losses focus on learning class-discriminative features while overlooking the commonalities shared across classes which are potentially useful in describing and matching samples. Moreover, they ignore the implicit hierarchy of categories in real-world datasets, where similar subordinate classes can be grouped together. In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss. This allows our model to capture both class-discriminative features and class-shared characteristics without breaking the implicit data hierarchy. We evaluate our method on five established image retrieval datasets such as In-Shop and SOP. Results demonstrate that our hierarchical proxy-based loss framework improves the performance of existing proxy-based losses, especially on large datasets which exhibit strong hierarchical structure.

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