Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-ID
局部可見感知模型——VPM 論文摘要 論文中考慮了行人重新識別 (re-ID) 的一個現實問題，即局部行人重識別問題。在局部行人重識別場景下，需要探索數據庫圖像中包含整體人物的比例大小。如果直接將局部行人圖像與整體行人圖像進行比較，則極端空間錯位問題將顯著地損害特徵表徵學習的判別能力。針對這個問題，本文提出了一個局部可見感知模型 (VPM)，通過自監督學習感知可見區域內的特徵，來解決局部行人重識別
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相關文章
- 1. Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-id
- 2. 【ReID】Learning Discriminative Features with Multiple Granularities for Person Re-identification
- 3. 【ReID】Hierarchical and Efficient Learning for Person Re-Identification
- 4. 《Learning Part-based Convolutional Features for Person Re-identification》TPAMI2019
- 5. CVPR2019行人重識別論文筆記1904.00537：Learning Visibility-aware Part-level Features for Partial Person Re-id
- 6. 【Person Re-ID】Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
- 7. VPM 行人局部特徵可見感知學習ReID
- 8. Person Re-Identification (ReID), a New Facial Recognition Record
- 9. 【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
- 10. ReID簡記-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id