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【ReID】EANet: Enhancing Alignment for Cross-Domain Person Re-identification
時間 2021-01-15
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【ReID】EANet: Enhancing Alignment for Cross-Domain Person Re-identification 痛點 模型 實驗 問題 參考文獻 文章在PCB[1]方法的基礎上提出Part Aligned Pooling (PAP),和基於多任務訓練的Part Segmentation (PS)約束方法,一個用了pose keypoint estimation
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相關文章
1.
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
2.
EANet: Enhancing Alignment for Cross-Domain Person Re-identification閱讀筆記
3.
part-aligned系列論文:1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 論文
4.
【ReID】Hierarchical and Efficient Learning for Person Re-Identification
5.
【ReID】【Skimming】Unity Style Transfer for Person Re-Identification
6.
【ReID】Batch DropBlock Network for Person Re-identification and Beyond
7.
【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
8.
SYSU-MM01的多模態Re-ID數據集 異構Heterogeneous person reidentification
9.
Deeply-Learned Spatial Alignment for Person Re-Identification 論文閱讀記錄
10.
Person Re-Identification (ReID), a New Facial Recognition Record
>>更多相關文章<<