Re-ranking Person Re-identification with k-reciprocal Encoding
基于k倒排编码的reid重排研究，论文中，作者提到当用reid方法获得初始的top-k排序后，重排方法是提升reid准确率的有效手段，作者提出了一种重排方法，通过编码特征降低计算量，并引入Jaccard距离，与原始距离做加权进行最终的rank list计算，该方法的好处是不需要人工交互或者任何标签数据，即可对任意的reid方法应用该手段来提升reid性能，并且可以应用到大规模数据集上，作者在Mar
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相关文章
- 1. Re-ranking Person Re-identification with k-reciprocal Encoding
- 2. 1701.Re-ranking Person Re-identification with k-reciprocal Encoding--PRW 论文阅读笔记
- 3. Re-ranking Person Re-identification with k-reciprocal Encoding 重排序算法解析
- 4. 论文笔记（4）：（Re-ID）Re-ranking Person Re-identification with k-reciprocal Encoding
- 5. Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification阅读
- 6. part-aligned系列论文：1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 论文
- 7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 8. SYSU-MM01的多模态Re-ID数据集 异构Heterogeneous person reidentification
- 9. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identificat...
- 10. 【Person Re-ID】Beyond Part Models: Person Retrieval with Refined Part Pooling