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 当前的问题及概述： 现有的RGB-IR ReID模型侧重于通过共享特征嵌入、子空间学习或对抗性学习来弥补RGB与IR图像之间的差距。然而，这些方法并没有明确地忽略与ReID无关的频谱信息。此外，对抗性学习方法
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相关文章
- 1. 【ReID】Hierarchical and Efficient Learning for Person Re-Identification
- 2. 论文笔记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论文笔记
- 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简记-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id