End-to-end comparative attention networks for person re-identification
End-to-end comparative attention networks for person re-identification 该论文中简单的总结一下关于attention的部分 整体网络架构如下： CNN部分采用截断的预训练VGG Net，去除最后三层全连接层，得到特征X H为网络的输出向量（具体如何得到，后文有解释） 注意网络分支共享参数 使用端到端的学习方式，优化下面提到的多任
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相关文章
- 1. 1606.End--to-End Comparative Attention Networks for Person Re-identification 论文笔记
- 2. Attention-over-Attention Neural Networks for RC
- 3. Harmonious Attention Network for Person Re-Identification
- 4. Collaborative Attention Network for Person Re-identification
- 5. [ICCV2019] Co-segmentation Inspired Attention Networks for Video-based Person Re-identification
- 6. Second-order Non-local Attention Networks for Person Re-identification论文阅读
- 7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 8. attention model -- Hierarchical Attention Networks for Document Classification
- 9. Attention-over-Attention Neural Networks for Reading Comprehension 讯飞
- 10. HAN(Hierarchical Attention Networks for Document Classification)