The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification
Open accessAbstract
Prevailing deep convolutional neural networks (CNNs) for person re-IDentification (reID) are usually built upon ResNet or VGG backbones, which were originally designed for classification. Because reID is different from classification, the architecture should be modified accordingly. We propose to automatically search for a CNN architecture that is specifically suitable for the reID task. There are three aspects to be tackled. First, body structural information plays an important role in reID but it is not encoded in backbones. Second, Neural Architecture Search (NAS) automates the process of architecture design without human effort, but no existing NAS methods incorporate the structure information of input images. Third, reID is essentially a retrieval task but current NAS algorithms are merely designed for classification. To solve these problems, we propose a retrieval-based search algorithm over a specifically designed reID search space, named Auto-ReID. Our Auto-ReID enables the automated approach to find an efficient and effective CNN architecture for reID. Extensive experiments demonstrate that the searched architecture achieves state-of-the-art performance while reducing 50% parameters and 53% FLOPs compared to others.
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Going deeper with convolutions
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Scalable Person Re-identification: A Benchmark
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Neural Architecture Search with Reinforcement Learning
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Cited by
Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification
Rao, Y., Chen, G., Lu, J., & Zhou, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00106 · Cited by 380
Neural Feature Search for RGB-Infrared Person Re-Identification
Chen, Y., Wan, L., Li, Z., Jing, Q., & Sun, Z. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00065 · Cited by 253
Metadata
- Authors
- Quan, R., Dong, X., Wu, Y., Zhu, L., & Yang, Y.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 3749-3758
- OpenAlex
- W2986093954
- Cited by
- 285
- References
- 44
- FWCI
- 16.87
- Type
- conference paper
- Language
- en
- Open access
- green
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- Quan, R., Dong, X., Wu, Y., Zhu, L., & Yang, Y. (2019). Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2019.00385
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