2019 TCSVT之ReID：SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-id
SDL: Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-identificatio 當前的問題及概述： 現有的RGB-IR ReID模型側重於通過共享特徵嵌入、子空間學習或對抗性學習來彌補RGB與IR圖像之間的差距。然而，這些方法並沒有明確地忽略與ReID無關的頻譜信息。此外，對抗性學習方法
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相關文章
- 1. 【ReID】Hierarchical and Efficient Learning for Person Re-Identification
- 2. 論文筆記23 -- （ReID）Receptive Multi-granularity Representation for Person Re-Identification
- 3. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification
- 4. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 5. 【ReID】Batch DropBlock Network for Person Re-identification and Beyond
- 6. 【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
- 7. 【PLPR】Progressive Learning for Person Re-Identification with One Example
- 8. Learning Context Graph for Person Search
- 9. View Confusion Feature Learning for Person Re-identification
- 10. ReID簡記-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id