most citedInstance Cross Entropy for Deep Metric Learning

5 citations · 9 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20195 cited

Instance Cross Entropy for Deep Metric Learning

Xinshao Wang, Elyor Kodirov, Yang Hua +1

Loss functions play a crucial role in deep metric learning thus a variety of them have been proposed. Some supervise the learning process by pairwise or tripletwise similarity cons…

cs.CV20193 cited

ID-aware Quality for Set-based Person Re-identification

Xinshao Wang, Elyor Kodirov, Yang Hua +1

Set-based person re-identification (SReID) is a matching problem that aims to verify whether two sets are of the same identity (ID). Existing SReID models typically generate a feat…

cs.LG2019

Derivative Manipulation for General Example Weighting

Xinshao Wang, Elyor Kodirov, Yang Hua +1

Real-world large-scale datasets usually contain noisy labels and are imbalanced. Therefore, we propose derivative manipulation (DM), a novel and general example weighting approach…

cs.CV20191 cited

GAN-based Pose-aware Regulation for Video-based Person Re-identification

Alessandro Borgia, Yang Hua, Elyor Kodirov +1

Video-based person re-identification deals with the inherent difficulty of matching unregulated sequences with different length and with incomplete target pose/viewpoint structure.…

cs.LG2018

Deep Metric Learning by Online Soft Mining and Class-Aware Attention

Xinshao Wang, Yang Hua, Elyor Kodirov +2

Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learnin…