2018 CVPR：Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training
Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training 当前的问题及概述： 大多数现有的方法高度依赖bounding box,然而，由于实际场景的限制，目前的检测模型往往产生不准确的bounding box，进而避免了算法的性能。 本文提出了一种新的由粗到细的金字塔模型，该模型不仅融合了局部信息和全局信息，而且还强化了局
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相关文章
- 1. Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training阅读笔记
- 2. 【论文笔记】：Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training
- 3. SYSU-MM01的多模态Re-ID数据集 异构Heterogeneous person reidentification
- 4. Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training论文阅读翻译 - 2020ECCV
- 5. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 6. 【论文笔记】Accelerated Training for Massive Classification via Dynamic Class Selection
- 7. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning论文笔记
- 8. 目标检测动态训练算法 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之图像识别：Visible thermal person re-identification via dual-constrained top-ranking