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RASNet閱讀筆記:Learning Attentions: Residual Attentional Siamese Network for High Performance Online Vis
時間 2020-12-30
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CVPR2018 論文在此 論文作者在知乎也進行了詳細的介紹:https://zhuanlan.zhihu.com/p/34222060 RASNet使用三個attention機制對SiamFC特徵的空間和channel進行加權,分解特徵提取和判別性分析的耦合,用來提升判別能力。 摘要 最近目標跟蹤中通過離線訓練的方式來平衡跟蹤的精度和速度,顯示出了巨大的潛力。然而在在線跟蹤中,離線訓練出的模型很
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相關文章
1.
Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Track筆記
2.
Learning Attentions: Residual Attentional Siamese Network for High Performance 論文讀後感
3.
CVPR 2018 RASNet:《Learning Attentions: Residual Attentional Siamese Network for Tracking》論文筆記
4.
Siamese Attentional Keypoint Network for High Performance Visual Tracking--論文筆記
5.
High Performance Visual Tracking with Siamese Region Proposal Network 閱讀筆記
6.
SiameseRPN: High Performance Visual Tracking with Siamese Region Proposal Network論文閱讀筆記
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【論文閱讀筆記】Performance Guaranteed Network Accelerationvia High-Order Residual Quantization
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