Xavier Bitot
YOU?
Author Swipe
View article: Optimization of Rank Losses for Image Retrieval
Optimization of Rank Losses for Image Retrieval Open
In image retrieval, standard evaluation metrics rely on score ranking, e.g. 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…
View article: Optimization of Rank Losses for Image Retrieval
Optimization of Rank Losses for Image Retrieval Open
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 r…
View article: Hierarchical Average Precision Training for Pertinent Image Retrieval
Hierarchical Average Precision Training for Pertinent Image Retrieval Open
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 …
View article: Robust and Decomposable Average Precision for Image Retrieval
Robust and Decomposable Average Precision for Image Retrieval Open
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…