2019 TIP之ReID：Learning Modality-Specific Representations for Visible-Infrared Person Re-Identificati
Learning Modality-Specific Representations for Visible-Infrared Person Re-Identification 當前的問題及概述： 由於不同的視覺特徵，在異構模式下匹配行人非常具有挑戰性。 模型及loss： 2.1Overview： 圖中可以看到，本文1）爲每個域建立一個特定於模態的網絡和一個特定於模態的loss函數，以便在特徵提取
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相關文章
- 1. 論文閱讀：輕量級reid：Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 2. Learning Delicate Local Representations for Multi-Person Pose Estimation
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- 4. [PersonReID] Part-Aligned Representations for Person Re-Identification
- 5. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification
- 6. Re-ID閱讀筆記(一)：《Deeply-Learned Part-Aligned Representations for Person Re-Identification》
- 7. ReID簡記-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id
- 8. Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 9. 論文解讀：《Learning Generalisable Omni-Scale Representations for Person Re-Identification（OSNet）》
- 10. Part-Aligned Bilinear Representations for Person Re-identification 論文筆記