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- W4287255395 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." @default.
- W4287255395 created "2022-07-25" @default.
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- W4287255395 date "2021-03-24" @default.
- W4287255395 modified "2023-10-15" @default.
- W4287255395 title "Hierarchical Proxy-based Loss for Deep Metric Learning" @default.
- W4287255395 hasPublicationYear "2021" @default.
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