1 citations · 1 across the 3 of their papers we have counts for
5 papers
Improving Deep Metric Learning with Virtual Classes and Examples Mining
Pierre Jacob, David Picard, Aymeric Histace +1
In deep metric learning, the training procedure relies on sampling informative tuples. However, as the training procedure progresses, it becomes nearly impossible to sample relevan…
DIABLO: Dictionary-based Attention Block for Deep Metric Learning
Pierre Jacob, David Picard, Aymeric Histace +1
Recent breakthroughs in representation learning of unseen classes and examples have been made in deep metric learning by training at the same time the image representations and a c…
Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings
Pierre Jacob, David Picard, Aymeric Histace +1
Learning an effective similarity measure between image representations is key to the success of recent advances in visual search tasks (e.g. verification or zero-shot learning). Al…
Efficient Codebook and Factorization for Second Order Representation Learning
Pierre Jacob, David Picard, Aymeric Histace +1
Learning rich and compact representations is an open topic in many fields such as object recognition or image retrieval. Deep neural networks have made a major breakthrough during…
Leveraging Implicit Spatial Information in Global Features for Image Retrieval
Pierre Jacob, David Picard, Aymeric Histace +1
Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spat…