2019 CVPR之ReID：Re-ranking via Metric Fusion for Object Retrieval and Person Re-identification
Re-ranking via Metric Fusion for Object Retrieval and Person Re-identification 當前的問題及概述： 視覺因素的變化，如視點、姿勢、白光和背景，通常被認爲是re-ID任務的重要挑戰。儘管這些因素是有影響的，但關於它們如何影響認知系統的定量研究仍然缺乏。 本文通過探究viewpoint對系統的影響進行探究，有兩大突出貢獻，首
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相關文章
- 1. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 2. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 3. DIVERSITY REGULARIZED METRIC LEARNING FOR PERSON RE-IDENTIFICATION（ICIP2016）
- 4. CVPR 2019 paper
- 5. Official Program for CVPR 2015
- 6. [CVPR 2019]Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
- 7. 2019 CVPR之ReID：Densely Semantically Aligned Person Re-Identification
- 8. part-aligned系列論文：1707.ICCV.Pedestrian Alignment Network for Large-scale Person Re-identification 論文
- 9. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 10. 2019 IET之ReID：HPILN: a feature learning framework for cross-modality person re-identification