Re-ranking Person Re-identification with k-reciprocal Encoding
基於k倒排編碼的reid重排研究，論文中，作者提到當用reid方法獲得初始的top-k排序後，重排方法是提升reid準確率的有效手段，作者提出了一種重排方法，通過編碼特徵降低計算量，並引入Jaccard距離，與原始距離做加權進行最終的rank list計算，該方法的好處是不需要人工交互或者任何標籤數據，即可對任意的reid方法應用該手段來提升reid性能，並且可以應用到大規模數據集上，作者在Mar
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相關文章
- 1. Re-ranking Person Re-identification with k-reciprocal Encoding
- 2. 1701.Re-ranking Person Re-identification with k-reciprocal Encoding--PRW 論文閱讀筆記
- 3. Re-ranking Person Re-identification with k-reciprocal Encoding 重排序算法解析
- 4. 論文筆記（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閱讀
- 6. part-aligned系列論文：1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 論文
- 7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 8. SYSU-MM01的多模態Re-ID數據集 異構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