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Attention-Based Neural Architecture Search for Person Re-Identification.
- Source :
- IEEE Transactions on Neural Networks & Learning Systems; Nov2022, Vol. 33 Issue 11, p6627-6639, 13p
- Publication Year :
- 2022
-
Abstract
- Recent years have witnessed significant progress of person reidentification (reID) driven by expert-designed deep neural network architectures. Despite the remarkable success, such architectures often suffer from high model complexity and time-consuming pretraining process, as well as the mismatches between the image classification-driven backbones and the reID task. To address these issues, we introduce neural architecture search (NAS) into automatically designing person reID backbones, i.e., reID-NAS, which is achieved via automatically searching attention-based network architectures from scratch. Different from traditional NAS approaches that originated for image classification, we design a reID-based search space as well as a search objective to fit NAS for the reID tasks. In terms of the search space, reID-NAS includes a lightweight attention module to precisely locate arbitrary pedestrian bounding boxes, which is automatically added as attention to the reID architectures. In terms of the search objective, reID-NAS introduces a new retrieval objective to search and train reID architectures from scratch. Finally, we propose a hybrid optimization strategy to improve the search stability in reID-NAS. In our experiments, we validate the effectiveness of different parts in reID-NAS, and show that the architecture searched by reID-NAS achieves a new state of the art, with one order of magnitude fewer parameters on three-person reID datasets. As a concomitant benefit, the reliance on the pretraining process is vastly reduced by reID-NAS, which facilitates one to directly search and train a lightweight reID model from scratch. [ABSTRACT FROM AUTHOR]
- Subjects :
- IDENTIFICATION
NETWORK-attached storage
COMPUTER architecture
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 33
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 160690181
- Full Text :
- https://doi.org/10.1109/TNNLS.2021.3082701