1 citations · 1 across the 5 of their papers we have counts for
5 papers · 1 filter
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
Marc Lafon, Clément Rambour, Nicolas Thome
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of i…
Optimization of Rank Losses for Image Retrieval
Elias Ramzi, Nicolas Audebert, Clément Rambour +3
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…
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Denis Coquenet, Clément Rambour, Emanuele Dalsasso +1
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs. However, they st…
Hierarchical Average Precision Training for Pertinent Image Retrieval
Elias Ramzi, Nicolas Audebert, Nicolas Thome +2
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…
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction
Vincent Le Guen, Clément Rambour, Nicolas Thome
State-of-the-art methods for optical flow estimation rely on deep learning, which require complex sequential training schemes to reach optimal performances on real-world data. In t…