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Hierarchical Average Precision Training for Pertinent Image Retrieval

Abstract : Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity. This paper introduces a new hierarchical AP training method for pertinent image retrieval (HAP-PIER). HAPPIER is based on a new H-AP metric, which leverages a concept hierarchy to refine AP by integrating errors' importance and better evaluate rankings. To train deep models with H-AP, we carefully study the problem's structure and design a smooth lower bound surrogate combined with a clustering loss that ensures consistent ordering. Extensive experiments on 6 datasets show that HAPPIER significantly outperforms state-of-the-art methods for hierarchical retrieval, while being on par with the latest approaches when evaluating fine-grained ranking performances. Finally, we show that HAPPIER leads to better organization of the embedding space, and prevents most severe failure cases of non-hierarchical methods. Our code is publicly available at: https://github.com/elias-ramzi/HAPPIER.
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https://hal.archives-ouvertes.fr/hal-03712933
Contributor : Elias Ramzi Connect in order to contact the contributor
Submitted on : Thursday, July 21, 2022 - 11:50:39 AM
Last modification on : Friday, August 5, 2022 - 2:54:00 PM

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  • HAL Id : hal-03712933, version 2
  • ARXIV : 2207.04873

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Elias Ramzi, Nicolas Audebert, Nicolas Thome, Clément Rambour, Xavier Bitot. Hierarchical Average Precision Training for Pertinent Image Retrieval. ECCV 2022, Oct 2022, Tel-Aviv, Israel. ⟨hal-03712933v2⟩

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