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Learning Attentions: Residual Attentional Siamese Network for High Performance Online Visual Track筆記
時間 2021-07-12
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該文是注意力機制應用的巔峯。簡單的說本文就是:Siamese+CSR-DCF 該文首先對SRDCF.CSR-DCF等使用了特徵加權的非深度學習方法和SimaseFC等的深度學習方法進行了介紹,指出非深度學習方法都是基於手動特徵,然後優化理論的傳統方法,而Siamese等深度學習方法則沒有使用注意機制,從而無法消除邊緣效應。該文將兩類跟蹤方法的優點進行結合並做了很多加強。代碼地址(要等到六月份..)
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相關文章
1.
RASNet閱讀筆記:Learning Attentions: Residual Attentional Siamese Network for High Performance Online Vis
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
7.
High Performance Visual Tracking with Siamese Region Proposal Network—CVPR2018 閱讀
8.
EDCF閱讀筆記:Reinforced Representation Learning for High Performance Visual Tracking
9.
《Siamese RPN:High Performance Visual Tracking with Siamese Region Proposal Network》論文筆記
10.
走進VOT--《High Performance Visual Tracking with Siamese Region Proposal Network》閱讀翻譯
>>更多相關文章<<