Ponce-López, V;
Burghardt, T;
Hannunna, S;
Damen, D;
Masullo, A;
Mirmehdi, M;
(2019)
Semantically selective augmentation for deep compact person re-identification.
In:
Computer Vision – ECCV 2018 Workshops. ECCV 2018.
(pp. pp. 551-561).
Springer: Cham, Switzerland.
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Abstract
We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional generative adversarial network (DCGAN), whose discriminator is constrained by a semantic classifier to explicitly control the domain specificity of the generation process. Thereby, we encode information in the classifier network which can be utilized to steer adversarial synthesis, and which fuels our CondenseNet ID-network training. We provide a quantitative and qualitative analysis of the approach and its variants on a number of datasets, obtaining results that outperform the state-of-the-art on the LIMA dataset for long-term monitoring in indoor living spaces.
| Type: | Proceedings paper |
|---|---|
| Title: | Semantically selective augmentation for deep compact person re-identification |
| ISBN-13: | 9783030110116 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-11012-3_41 |
| Publisher version: | https://doi.org/10.1007/978-3-030-11012-3_41 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Person re-identification, Selective augmentation, Face filtering, Adversarial synthesis, Deep compression |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10115349 |
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