Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification
Guangcong Wang, Jian-Huang Lai, Wenqi Liang, Guangrun Wang
2020年份
9顶会引用
摘要
- Corresponding author bility in real-world scenarios. When applied to a new scenario, they suffer from serious performance degradation, e.g., from 92.0% to 47.5% on the Market-1501 dataset [52]. How to transfer the knowledge from a labeled source domain to an unlabeled target domain for person re-ID and how to exploit the specific knowledge from an unlabeled target domain to boost person re-ID methods become hot topics in the current person re-ID community. It is widely called cross-domain person re-ID.
Recently, lots of cross-domain person re-ID methods
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引用它的顶会 Paper9
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- Solving Inefficiency of Self-supervised Representation LearningGuangrun Wang, Keze Wang, Guangcong Wang, Philip H. S. Torr 等ICCV 2021 · 被引用 64 次
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它引用的顶会 Paper6
- Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and AdaptationYu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin, Yu-Chiang Frank WangICCV 2019 · 被引用 204 次
- Instance-Guided Context Rendering for Cross-Domain Person Re-IdentificationYanbei Chen, Xiatian Zhu, Shaogang GongICCV 2019 · 被引用 178 次
- Bilinear Attention Networks for Person RetrievalPengfei Fang, Jieming Zhou, Soumava Kumar Roy, Lars Petersson 等ICCV 2019 · 被引用 154 次
- A Novel Unsupervised Camera-Aware Domain Adaptation Framework for Person Re-IdentificationLei Qi, Lei Wang, Jing Huo, Luping Zhou 等ICCV 2019 · 被引用 144 次
- Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency LearningAncong Wu, Wei-Shi Zheng, Jian-Huang LaiICCV 2019 · 被引用 114 次
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