End-to-end comparative attention networks for person re-identification
End-to-end comparative attention networks for person re-identification 該論文中簡單的總結一下關於attention的部分 整體網絡架構如下： CNN部分採用截斷的預訓練VGG Net，去除最後三層全連接層，得到特徵X H爲網絡的輸出向量（具體如何得到，後文有解釋） 注意網絡分支共享參數 使用端到端的學習方式，優化下面提到的多任
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相關文章
- 1. 1606.End--to-End Comparative Attention Networks for Person Re-identification 論文筆記
- 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論文閱讀
- 7. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 8. attention model -- Hierarchical Attention Networks for Document Classification
- 9. Attention-over-Attention Neural Networks for Reading Comprehension 訊飛
- 10. HAN(Hierarchical Attention Networks for Document Classification)