Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (11): 3573-3579.DOI: 10.11772/j.issn.1001-9081.2021122124
Special Issue: 第二十一届中国虚拟现实大会
• ChinaVR 2021 • Previous Articles Next Articles
Received:
2021-12-17
Revised:
2022-02-28
Accepted:
2022-03-07
Online:
2022-05-17
Published:
2022-11-10
Contact:
Yongjian LIAN
About author:
GENG Yanbing, born in 1980, Ph. D., lecturer. Her research interests include image processing, pattern recognition, artificial intelligence.Supported by:
通讯作者:
廉永健
作者简介:
耿艳兵(1980—),女,河南漯河人,讲师,博士,CCF会员,主要研究方向:图像处理、模式识别、人工智能基金资助:
CLC Number:
Yanbing GENG, Yongjian LIAN. Cross‑resolution person re‑identification by generative adversarial network based on multi‑granularity features[J]. Journal of Computer Applications, 2022, 42(11): 3573-3579.
耿艳兵, 廉永健. 基于多粒度特征生成对抗网络的跨分辨率行人重识别[J]. 《计算机应用》唯一官方网站, 2022, 42(11): 3573-3579.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122124
方法 | MLR‑CUHK03 | |||
---|---|---|---|---|
SSIM | PSNR/dB | LPIPS | rank‑1/% | |
GAN | 0.30 | 13.9 | 0.42 | 47.8 |
GAN+自注意力 | 0.61 | 19.2 | 0.19 | 65.3 |
GAN+识别网络(高层损失) | 0.41 | 18.1 | 0.23 | 76.9 |
GAN+识别网络(中低层损失) | 0.57 | 20.9 | 0.16 | 78.1 |
GAN+识别网络 (高层&中低层损失) | 0.61 | 21.3 | 0.13 | 80.5 |
GAN+自注意力+识别网络 (高层损失) | 0.69 | 22.5 | 0.12 | 85.5 |
GAN+自注意力+识别网络 (中低层损失) | 0.71 | 22.8 | 0.08 | 85.3 |
GAN+自注意力+识别网络 (高层&中低层损失) | 0.89 | 23.5 | 0.06 | 87.1 |
Tab. 1 Ablation experimental results of multi?granularity feature fusion based generative adversarial network on MLR?CUHK03 dataset
方法 | MLR‑CUHK03 | |||
---|---|---|---|---|
SSIM | PSNR/dB | LPIPS | rank‑1/% | |
GAN | 0.30 | 13.9 | 0.42 | 47.8 |
GAN+自注意力 | 0.61 | 19.2 | 0.19 | 65.3 |
GAN+识别网络(高层损失) | 0.41 | 18.1 | 0.23 | 76.9 |
GAN+识别网络(中低层损失) | 0.57 | 20.9 | 0.16 | 78.1 |
GAN+识别网络 (高层&中低层损失) | 0.61 | 21.3 | 0.13 | 80.5 |
GAN+自注意力+识别网络 (高层损失) | 0.69 | 22.5 | 0.12 | 85.5 |
GAN+自注意力+识别网络 (中低层损失) | 0.71 | 22.8 | 0.08 | 85.3 |
GAN+自注意力+识别网络 (高层&中低层损失) | 0.89 | 23.5 | 0.06 | 87.1 |
方法 | MLR‑Market1501 | MLR‑CUHK03 | MLR‑VIPeR | MLR‑DukeMTMC‑reID | CAVIAR | |||||
---|---|---|---|---|---|---|---|---|---|---|
rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | |
CamStyle | 74.5 | 88.6 | 69.1 | 89.6 | 34.4 | 56.8 | 64.0 | 78.1 | 32.1 | 72.3 |
FD‑GAN | 79.6 | 91.6 | 73.4 | 92.8 | 39.1 | 62.1 | 67.5 | 82.0 | 33.5 | 71.4 |
SLD2L | ― | ― | ― | ― | 20.3 | 44.0 | ― | ― | 18.4 | 44.8 |
SING | 74.4 | 87.8 | 67.7 | 90.7 | 33.5 | 57.0 | 65.2 | 80.1 | 33.5 | 72.7 |
CSR‑GAN | 76.4 | 88.5 | 71.3 | 92.1 | 37.2 | 62.3 | 67.6 | 81.4 | 34.7 | 72.5 |
JUDEA | ― | ― | 26.2 | 58.0 | 26.0 | 55.1 | ― | ― | 22.0 | 60.1 |
SDF | ― | ― | 22.2 | 48.0 | 9.3 | 38.1 | ― | ― | 14.3 | 37.5 |
RAIN | ― | ― | 78.9 | 97.3 | 42.5 | 68.3 | ― | ― | 42.0 | 77.3 |
CAD | 83.7 | 92.7 | 82.1 | 97.4 | 43.1 | 68.2 | 75.6 | 86.7 | 42.8 | 76.2 |
INTACT | 88.1 | 95.0 | 86.4 | 97.4 | 46.2 | 73.1 | 81.2 | 90.1 | 44.0 | 81.8 |
本文方法 | 88.9 | 96.3 | 87.1 | 97.5 | 46.9 | 73.6 | 82.1 | 91.8 | 44.9 | 83.2 |
Tab. 2 Rank?1 and rank?5 accuracies on CMC of proposed method and existing similar methods on different datasets
方法 | MLR‑Market1501 | MLR‑CUHK03 | MLR‑VIPeR | MLR‑DukeMTMC‑reID | CAVIAR | |||||
---|---|---|---|---|---|---|---|---|---|---|
rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | rank‑1 | rank‑5 | |
CamStyle | 74.5 | 88.6 | 69.1 | 89.6 | 34.4 | 56.8 | 64.0 | 78.1 | 32.1 | 72.3 |
FD‑GAN | 79.6 | 91.6 | 73.4 | 92.8 | 39.1 | 62.1 | 67.5 | 82.0 | 33.5 | 71.4 |
SLD2L | ― | ― | ― | ― | 20.3 | 44.0 | ― | ― | 18.4 | 44.8 |
SING | 74.4 | 87.8 | 67.7 | 90.7 | 33.5 | 57.0 | 65.2 | 80.1 | 33.5 | 72.7 |
CSR‑GAN | 76.4 | 88.5 | 71.3 | 92.1 | 37.2 | 62.3 | 67.6 | 81.4 | 34.7 | 72.5 |
JUDEA | ― | ― | 26.2 | 58.0 | 26.0 | 55.1 | ― | ― | 22.0 | 60.1 |
SDF | ― | ― | 22.2 | 48.0 | 9.3 | 38.1 | ― | ― | 14.3 | 37.5 |
RAIN | ― | ― | 78.9 | 97.3 | 42.5 | 68.3 | ― | ― | 42.0 | 77.3 |
CAD | 83.7 | 92.7 | 82.1 | 97.4 | 43.1 | 68.2 | 75.6 | 86.7 | 42.8 | 76.2 |
INTACT | 88.1 | 95.0 | 86.4 | 97.4 | 46.2 | 73.1 | 81.2 | 90.1 | 44.0 | 81.8 |
本文方法 | 88.9 | 96.3 | 87.1 | 97.5 | 46.9 | 73.6 | 82.1 | 91.8 | 44.9 | 83.2 |
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