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 簡述： 現有的方法通常只學習了跨模態特徵，而忽略了圖像層次的差異。生成圖像的思路目前有些方法採用GAN生成交叉模態圖像，但會破壞局部結構並引入噪聲。本文通過生成的RGB圖像的灰度圖作爲輔助，以減少RGB與IR圖像的large
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相關文章
- 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 論文筆記
- 4. SYSU-MM01的多模態Re-ID數據集 異構Heterogeneous person reidentification
- 5. 學習筆記HHL：Generalizing a Person Retrieval Model--Hetero-Homogeneous Learning UDA鄭良2018
- 6. Unsupervised Person Re-identification via Multi-label Classification
- 7. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 8. 2018 CVPR：Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
- 9. 【論文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