Xavier Bitot — accepted AI-conference papers

2022

Hierarchical Average Precision Training for Pertinent Image Retrieval

Elias Ramzi, Nicolas Audebert, Nicolas Thome, Clément Rambour, Xavier Bitot

ECCV 2022poster

"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 (HAPPIER). HAPPIER is based on a n…

Cited by 12 Source ↗PDF ↗code ↗Scholar ↗

2021

Robust and Decomposable Average Precision for Image Retrieval

Elias Ramzi, Nicolas THOME, Clément Rambour, Nicolas Audebert, Xavier Bitot

NeurIPS 2021poster

In image retrieval, standard evaluation metrics rely on score ranking, e.g. average precision (AP). In this paper, we introduce a method for robust and decomposable average precision (ROADMAP) addressing two major challenges for end-to-end training of deep neural networks with AP: non-differentiabil…

Cited by 39 Source ↗PDF ↗code ↗Scholar ↗