Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1391-1397.DOI: 10.11772/j.issn.1001-9081.2021030459
• Artificial intelligence • Previous Articles Next Articles
Daili CHEN1,2, Guoliang XU1,2()
Received:
2021-03-26
Revised:
2021-06-22
Accepted:
2021-06-23
Online:
2022-06-11
Published:
2022-05-10
Contact:
Guoliang XU
About author:
CHEN Daili, born in 1996, M. S. candidate. Her research interests include computer vision.通讯作者:
许国良
作者简介:
陈代丽(1996—),女,四川宜宾人,硕士研究生,主要研究方向:计算机视觉CLC Number:
Daili CHEN, Guoliang XU. Cross-domain person re-identification method based on attention mechanism with learning intra-domain variance[J]. Journal of Computer Applications, 2022, 42(5): 1391-1397.
陈代丽, 许国良. 基于注意力机制学习域内变化的跨域行人重识别方法[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1391-1397.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030459
方法 | DukeMTMC-reID→ Market-1501 | Market-1501→DukeMTMC-reID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
Baseline | 20.2 | 45.6 | 19.4 | 48.7 |
Baseline+GFB | 47.9 | 78.6 | 43.4 | 65.8 |
Baseline+RAB | 49.5 | 78.9 | 44.3 | 66.4 |
Baseline+GFB+RAB | 49.5 | 80.1 | 44.2 | 67.7 |
Tab. 1 Performance comparison of different branches
方法 | DukeMTMC-reID→ Market-1501 | Market-1501→DukeMTMC-reID | ||
---|---|---|---|---|
mAP | Rank-1 | mAP | Rank-1 | |
Baseline | 20.2 | 45.6 | 19.4 | 48.7 |
Baseline+GFB | 47.9 | 78.6 | 43.4 | 65.8 |
Baseline+RAB | 49.5 | 78.9 | 44.3 | 66.4 |
Baseline+GFB+RAB | 49.5 | 80.1 | 44.2 | 67.7 |
方法 | DukeMTMC-reID → Market-1501 | Market-1501 → DukeMTMC-reID | ||||||
---|---|---|---|---|---|---|---|---|
Rank-1 | Rank-5 | Rank-10 | mAP | Rank-1 | Rank-5 | Rank-10 | mAP | |
LOMO | 27.2 | 41.6 | 49.1 | 8.0 | 12.3 | 21.3 | 26.6 | 4.8 |
BoW | 35.8 | 52.4 | 60.3 | 14.8 | 17.1 | 28.8 | 34.9 | 8.3 |
SSG | 80.0 | 90.0 | 92.4 | 58.3 | 73.0 | 80.6 | 83.2 | 53.4 |
MAR | 67.7 | 81.9 | — | 40.0 | 67.1 | 79.8 | — | 48.0 |
SPGAN | 51.5 | 70.1 | 76.8 | 22.8 | 41.1 | 56.6 | 63.0 | 22.3 |
PTGAN | 38.6 | — | 66.7 | — | 27.4 | — | 50.7 | — |
CamStyle | 58.8 | 78.2 | 84.3 | 27.4 | 48.4 | 62.5 | 68.9 | 25.1 |
CSGAN | 61.9 | 78.8 | 84.4 | 29.7 | 47.8 | 63.5 | 67.2 | 26.3 |
ECN | 75.1 | 78.8 | 84.0 | 43.0 | 63.3 | 75.8 | 80.4 | 40.4 |
D-MMD | 70.6 | 87.0 | 90.2 | 48.8 | 63.5 | 78.8 | 83.9 | 46.0 |
ICE | 90.8 | 95.8 | 97.2 | 73.8 | 80.2 | 88.5 | 91.6 | 66.4 |
本文方法 | 80.1 | 91.1 | 93.9 | 49.5 | 67.7 | 79.1 | 82.5 | 44.2 |
Tab. 2 Performance comparison of different methods on Market-1501 and DukeMTMC-reID
方法 | DukeMTMC-reID → Market-1501 | Market-1501 → DukeMTMC-reID | ||||||
---|---|---|---|---|---|---|---|---|
Rank-1 | Rank-5 | Rank-10 | mAP | Rank-1 | Rank-5 | Rank-10 | mAP | |
LOMO | 27.2 | 41.6 | 49.1 | 8.0 | 12.3 | 21.3 | 26.6 | 4.8 |
BoW | 35.8 | 52.4 | 60.3 | 14.8 | 17.1 | 28.8 | 34.9 | 8.3 |
SSG | 80.0 | 90.0 | 92.4 | 58.3 | 73.0 | 80.6 | 83.2 | 53.4 |
MAR | 67.7 | 81.9 | — | 40.0 | 67.1 | 79.8 | — | 48.0 |
SPGAN | 51.5 | 70.1 | 76.8 | 22.8 | 41.1 | 56.6 | 63.0 | 22.3 |
PTGAN | 38.6 | — | 66.7 | — | 27.4 | — | 50.7 | — |
CamStyle | 58.8 | 78.2 | 84.3 | 27.4 | 48.4 | 62.5 | 68.9 | 25.1 |
CSGAN | 61.9 | 78.8 | 84.4 | 29.7 | 47.8 | 63.5 | 67.2 | 26.3 |
ECN | 75.1 | 78.8 | 84.0 | 43.0 | 63.3 | 75.8 | 80.4 | 40.4 |
D-MMD | 70.6 | 87.0 | 90.2 | 48.8 | 63.5 | 78.8 | 83.9 | 46.0 |
ICE | 90.8 | 95.8 | 97.2 | 73.8 | 80.2 | 88.5 | 91.6 | 66.4 |
本文方法 | 80.1 | 91.1 | 93.9 | 49.5 | 67.7 | 79.1 | 82.5 | 44.2 |
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