2020 TIFS之ReID:Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learnin
Visible-Infrared Person Re-Identification via Homogeneous Augmented Tri-Modal Learning 简述： 现有的方法通常只学习了跨模态特征，而忽略了图像层次的差异。生成图像的思路目前有些方法采用GAN生成交叉模态图像，但会破坏局部结构并引入噪声。本文通过生成的RGB图像的灰度图作为辅助，以减少RGB与IR图像的large
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相关文章
- 1. 2020 TIFS-ReID:Bi-Directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identific
- 2. 2020 CVPR 之ReID：AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identi
- 3. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 4. SYSU-MM01的多模态Re-ID数据集 异构Heterogeneous person reidentification
- 5. 学习笔记HHL：Generalizing a Person Retrieval Model--Hetero-Homogeneous Learning UDA郑良2018
- 6. Unsupervised Person Re-identification via Multi-label Classification
- 7. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning论文笔记
- 8. 2018 CVPR：Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
- 9. 【论文2】AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
- 10. 2019 ArXiv之ReID：Attend to the Difference: Cross-Modality Person Re-identification via Contrastive