1705.Person Re-Identification by Deep Joint Learning of Multi-Loss Classification 論文閱讀筆記
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification 本文采用多loss分類聯合訓練同時學習行人條紋局部特徵和全局特徵，受益於局部和全局學習到的特徵具有互補性，因此得到的特徵更具區分性。具體框架如下： 提出的JLML（Joint Learning Multi-Loss）模型框架如下： 1.兩個分支，
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相關文章
- 1. 1705.IAN- The Individual Aggregation Network for Person Search 論文閱讀筆記
- 2. Machine Learning & Deep Learning 論文閱讀筆記
- 3. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 4. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 5. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 6. Person Re-Identification by Deep Joint Learning of Multi-Loss Classification——準研究生論文週報
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- 8. 論文閱讀筆記（五十二）：Outline Objects using Deep Reinforcement Learning
- 9. 論文閱讀筆記《Deep Active Learning for Civil Infrastructure Defect Detection and Classification》
- 10. 【論文閱讀】-2019-CLASSIFICATION OF AERIAL POINT CLOUDS WITH DEEP LEARNING