DIVERSITY REGULARIZED METRIC LEARNING FOR PERSON RE-IDENTIFICATION（ICIP2016）
基于多元正则化度量学习的行人再识别 ABSTRACT 度量学习是行人再识别的有效方法。 它利用潜在因素找到适合测量距离的合适空间。 通常，少数因素不足以匹配行人，而大量因素导致高计算成本。 在本文中，为了平衡这种操作，提出了一种新的多元正则化距离度量学习方法。 对于特征表示，从源图像中提取局部判别特征，并且提出邻接最大约束以处理未对准的问题。 然后利用多样正则化容器通过使潜在因子不相关来学习度量，
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相关文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 2. Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-ide
- 3. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 4. Efficient PSD Constrained Asymmetric Metric Learning for Person Re-identification(MLAPG)
- 5. Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification阅读笔记
- 6. 论文笔记《Deep Cosine Metric Learning for Person Re-Identification》
- 7. Person Re-identification by Local Maximal Occurrence Representation and Metric Learning（LOMO+XQDA）
- 8. Survey and experimental study on metric learning methods
- 9. Large-scale Distance Metric Learning with Uncertainty
- 10. Few-shot Learning with Meta Metric Learners