ICCV 2017 DSiam:《Learning Dynamic Siamese Network for Visual Object Tracking》論文筆記
理解出錯之處望不吝指正。 本文模型叫做DSiam。作者提出一個Dynamic Siamese Network，可以使用一個transformation learning model來在線學習目標的外觀變化並且壓制背景信息。本文的另一個創新點在於作者提出了一個自適應聚合各個層的特徵的方法（elementwise multi-layer fusion）。並且，本模型不用像以往的基於Siames
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- 9. 《Siamese RPN：High Performance Visual Tracking with Siamese Region Proposal Network》論文筆記
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相關文章
- 1. DSiam閱讀筆記：Learning Dynamic Siamese Network for Visual Object Tracking
- 2. Learning Dynamic Siamese Network for Visual Object Tracking—ICCV2017 閱讀
- 3. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》論文筆記
- 4. 論文筆記：Learning Dynamic Memory Networks for Object Tracking
- 5. ICCV 2017 EAST:《Learning Policies for Adaptive Tracking with Deep Feature Cascades》論文筆記
- 6. ECCV 2018 《Triplet Loss in Siamese Network for Object Tracking》論文筆記
- 7. Siamese Attentional Keypoint Network for High Performance Visual Tracking--論文筆記
- 8. ICCV 2017 UCT:《UCT: Learning Unified Convolutional Networks forReal-time Visual Tracking》論文筆記
- 9. 《Siamese RPN：High Performance Visual Tracking with Siamese Region Proposal Network》論文筆記
- 10. CVPR 2018 RASNet:《Learning Attentions: Residual Attentional Siamese Network for Tracking》論文筆記