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Person Transfer GAN to Bridge Domain Gap for Person Re-Identification(PTGAN+MSMT17)
時間 2021-01-07
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論文分爲數據集和圖像風格遷移算法(兩個數據集之間)兩部分: 這是屬於無監督的遷移,GAN Motivation: 1.數據集和現實的區別:1.規模小2.場景單一 3.光照單一 解決:因此提出了更爲複雜的數據集MSMT17。 2.想解決訓練集測試集不均衡的問題:(目前訓練測試集基本上時1:1的比例) 方法:重用之前的別的數據集訓練。但是數據集之前的gap導致識別率低。 Multi-SceneMult
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相關文章
1.
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
2.
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3.
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification
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6.
【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
7.
Asymmetric Co-Teaching for Unsupervised Cross-Domain Person Re-Identification
8.
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
9.
ICCV2019-行人重識別-Instance-Guided Context Rendering for Cross-Domain Person Re-Identification
10.
1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
>>更多相關文章<<