1606.End--to-End Comparative Attention Networks for Person Re-identification 论文笔记
简介 参考人类视觉机制：即重复性的抓住某一区域主要信息来发现比较对象的相似性和差异性，来发现最匹配的目标， 利用LSTM网络设计注意力机制，增加了时空信息，设计端到端训练的比较性注意力网络（CAN），训练阶段，该网络采用online triplet损失，可以使LSTM网络部分对同一ID的对个显著性局部区域比较，并pull他们之间的相似性，而对不同ID的两个行人的多个显著性区域也进行学习，使之pus
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相关文章
- 1. End-to-end comparative attention networks for person re-identification
- 2. part-aligned系列论文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 论文笔记
- 3. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
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- 5. Attention-over-Attention Neural Networks for Reading Comprehension论文笔记
- 6. Second-order Non-local Attention Networks for Person Re-identification论文阅读
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- 8. [论文笔记]Self-Attention Generative Adversarial Networks
- 9. SAGAN(Self-Attention Generative Adversarial Networks)论文笔记
- 10. GRAPH ATTENTION NETWORKS--论文阅读笔记