2018 CVPR：Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training 當前的問題及概述： 大多數現有的方法高度依賴bounding box,然而，由於實際場景的限制，目前的檢測模型往往產生不準確的bounding box，進而避免了算法的性能。 本文提出了一種新的由粗到細的金字塔模型，該模型不僅融合了局部信息和全局信息，而且還強化了局
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相關文章
- 1. Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training閱讀筆記
- 2. 【論文筆記】：Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
- 3. SYSU-MM01的多模態Re-ID數據集 異構Heterogeneous person reidentification
- 4. Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training論文閱讀翻譯 - 2020ECCV
- 5. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 6. 【論文筆記】Accelerated Training for Massive Classification via Dynamic Class Selection
- 7. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 8. 目標檢測動態訓練算法 Dynamic R-CNN | Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
- 9. 2018 IJCAI之ReID：Cross-Modality Person Re-Identification with Generative Adversarial Training
- 10. 2018 IJCAI之圖像識別：Visible thermal person re-identification via dual-constrained top-ranking