activity
20112022
most citedSearching in one billion vectors: re-rank with source coding

34 citations · 38 across the 5 of their papers we have counts for

collaborators

9 papers

cs.LG20221 cited

Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis

Laurent Amsaleg, Oussama Chelly, Michael E. Houle +3

Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection,…

cs.CV2020

Joint Learning of Assignment and Representation for Biometric Group Membership

Marzieh Gheisari, Teddy Furon, Laurent Amsaleg

This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the iden…

cs.CR2020

Group Membership Verification with Privacy: Sparse or Dense?

Marzieh Gheisari, Teddy Furon, Laurent Amsaleg

Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy f…

cs.CV2019

Walking on the Edge: Fast, Low-Distortion Adversarial Examples

Hanwei Zhang, Yannis Avrithis, Teddy Furon +1

Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural…

cs.CV2019

Privacy Preserving Group Membership Verification and Identification

Marzieh Gheisari, Teddy Furon, Laurent Amsaleg

When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, gro…

cs.MM20193 cited

Exquisitor: Interactive Learning at Large

Björn Þór Jónsson, Omar Shahbaz Khan, Hanna Ragnarsdóttir +6

Increasing scale is a dominant trend in today's multimedia collections, which especially impacts interactive applications. To facilitate interactive exploration of large multimedia…