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pro vyhledávání: '"Bitot, Xavier"'
Autor:
Ramzi, Elias, Audebert, Nicolas, Rambour, Clément, Araujo, André, Bitot, Xavier, Thome, Nicolas
In image retrieval, standard evaluation metrics rely on score ranking, \eg average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work we introduce a general framework for robust and decomposable rank losses
Externí odkaz:
http://arxiv.org/abs/2309.08250
Publikováno v:
ECCV 2022, Oct 2022, Tel-Aviv, Israel
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 i
Externí odkaz:
http://arxiv.org/abs/2207.04873
Publikováno v:
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Dec 2021, Sydney, Australia
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 o
Externí odkaz:
http://arxiv.org/abs/2110.01445