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19 CVPR之ReID:Learning to Reduce Dual-level Discrepancy for Infrared-Visible Person Re-identification
時間 2021-01-07
標籤
GAN
多模態
ReID
深度學習
計算機視覺
人工智能
欄目
快樂工作
简体版
原文
原文鏈接
Learning to Reduce Dual-level Discrepancy for Infrared-Visible Person Re-identification 目前的問題: 由於RGB-IV的ReID任務中,除了視點變化、姿態變化和變形所造成的人的外貌差異外,還有由於光譜相機成像過程的不同而產生的額外模態差異,以前的方法嘗試使用特徵級約束同時減少外觀和模態差異。然而,僅使用特徵級約
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相關文章
1.
[CVPR 2018]Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
2.
1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
3.
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
4.
2019 CVPR之ReID:Towards Rich Feature Discovery with Class Activation Maps Augmentation for Person Re-
5.
2020 CVPR 之ReID:AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identi
6.
2019 CVPR之ReID:Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Id
7.
Person Transfer GAN to Bridge Domain Gap for Person Re-identification
8.
Stepwise Metric Promotion for Unsupervised Video Person Re-identification
9.
2019 CVPR之ReID:Densely Semantically Aligned Person Re-Identification
10.
SYSU-MM01的多模態Re-ID數據集 異構Heterogeneous person reidentification
>>更多相關文章<<