Journal article

Identity Adaptation for Person Re-Identification

Q Ke, M Bennamoun, H Rahmani, S An, F Sohel, F Boussaid

IEEE Access | Institute of Electrical and Electronics Engineers | Published : 2018

Abstract

Person re-identification (re-ID), which aims to identify the same individual from a gallery collected with different cameras, has attracted increasing attention in the multimedia retrieval community. Current deep learning methods for person re-ID focus on learning classification models on training identities to obtain an ID-discriminative embedding (IDE) extractor, which is used to extract features from testing images for re-ID. The IDE features of the testing identities might not be discriminative due to that the training identities are different from the testing identities. In this paper, we introduce a new ID-adaptation network (ID-AdaptNet), which aims to improve the discriminative power..

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Grants

Awarded by Australian Research Council


Funding Acknowledgements

This work was supported by the Australian Research Council under Grant DP150100294, Grant DP150104251, and Grant DE120102960.