2019 CVPR之ReID：Towards Rich Feature Discovery with Class Activation Maps Augmentation for Person Re-
Towards Rich Feature Discovery with Class Activation Maps Augmentation for Person Re-Identification 當前的問題及概述： 在Re-ID任務中，人物間較小的差異需要模型來捕獲足夠細粒度的特性。 基於此，1，本文通過CAM擴展了baseline重建模型的激活範圍，以探索豐富的視覺線索。2，提出了一種Ove
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- 1. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification
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相關文章
- 1. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification
- 2. CVPR 2020之ReID：Cross-modality Person re-identification with Shared-Specific Feature Transfer
- 3. 2019 CVPR之ReID：Densely Semantically Aligned Person Re-Identification
- 4. Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification
- 5. 2019 CVPR之ReID:Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Id
- 6. Self-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identificatio
- 7. Re-ID: Learning Deep Feature Representation with Domain Guided Dropout for Person Re-identifiation
- 8. CVPR 2017:Large Margin Object Tracking with Circulant Feature Maps
- 9. 論文解讀：Self-training with progressive augmentation for unsupervised person re-ID（PAST）
- 10. View Confusion Feature Learning for Person Re-identification