Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
在這篇論文中，作者提出了通過逐漸利用未標註樣本，來解決單標註樣本（one-shot）情況下的視頻行人重識別問題（video-based person re-ID）。這個方法簡單通用，在MARS和DukeMTMC兩個視頻行人重識別數據集上都達到了遠超 state-of-the art 的性能。 1. 論文概述 爲什麼需要關注單標註（one shot)樣本問題？ 目前大多行人重識別方法都依賴於完全的數
相關文章
- 1. 【EUG】Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learn
- 2. CVPR 2018 行人重識別：Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by S L
- 3. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 4. 【FSR】Feature Space Regularization for Person Re-Identification with One Sample
- 5. Unsupervised Person Re-identification by Soft Multilabel Learning
- 6. Improving Person Re-identification by Attribute and Identity Learning
- 7. Unsupervised Person Re-identification by Deep Learning Tracklet Association
- 8. Person search: Joint Detection and Identification Feature Learning for Person Search筆記
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- 10. OSCP Learning Notes - Exploit(6)
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相關文章
- 1. 【EUG】Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learn
- 2. CVPR 2018 行人重識別：Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by S L
- 3. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 4. 【FSR】Feature Space Regularization for Person Re-Identification with One Sample
- 5. Unsupervised Person Re-identification by Soft Multilabel Learning
- 6. Improving Person Re-identification by Attribute and Identity Learning
- 7. Unsupervised Person Re-identification by Deep Learning Tracklet Association
- 8. Person search: Joint Detection and Identification Feature Learning for Person Search筆記
- 9. 【Person Re-ID】Person Re-Identification by Deep Learning Multi-Scale Representations
- 10. OSCP Learning Notes - Exploit(6)