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論文解讀:Self-training with progressive augmentation for unsupervised person re-ID(PAST)
時間 2021-01-13
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引言 通過深度學習和大量帶標籤的訓練數據,行人再識別(Re-ID)取得了很大的進步。然而,將在標記數據的源域中訓練的模型調整爲僅可用未標記數據的目標域仍然是一項艱鉅的任務。在這項工作中,我們開發了一種具有漸進增強框架(PAST)的自訓練方法,以在目標數據集上逐步提升模型性能。特別地,我們的PAST框架包括兩個階段,即保留階段和提升階段。保留階段使用基於三元組的損失函數捕獲目標域數據點的局部結構,從
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相關文章
1.
Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification
2.
Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identificatio
3.
Unsupervised Data Augmentation For Consistency Training 論文閱讀
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【PLPR】Progressive Learning for Person Re-Identification with One Example
5.
《Beyond Part Models: Person Retrieval with Refined Part Pooling 》PCB論文解讀
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論文筆記:Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification
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【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
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【論文閱讀】 Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification
10.
[CVPR2019]Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification論文筆記
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