CREST: Convolutional Residual Learning for Visual Tracking

时间 2020-12-30

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简介视觉目标追踪，CREST，ICCV 2017 在目标跟踪界泰斗杨明玄教授的指导下，香港城市大学，阿德莱德大学的研究人员提出了基于残差学习的端到端的深度目标跟踪算法。本文已开源，项目地址请查阅：https://ybsong00.github.io/iccv17/index.html Github链接：https://github.com/ybsong00/CREST-Release Moti

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1. CREST: Convolutional Residual Learning for Visual Tracking
2. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》论文笔记
3. 目标跟踪-CREST: Convolutional Residual Learning for Visual Tracking
4. CREST: Convolutional Residual Learning for Visual Tracking ---- 文献翻译理解
5. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
6. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
7. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
8. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
9. 论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
10. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 阅读笔记
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1. CREST: Convolutional Residual Learning for Visual Tracking
2. ICCV 2017 CREST：《CREST: Convolutional Residual Learning for Visual Tracking》论文笔记
3. 目标跟踪-CREST: Convolutional Residual Learning for Visual Tracking
4. CREST: Convolutional Residual Learning for Visual Tracking ---- 文献翻译理解
5. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
6. MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
7. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
8. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
9. 论文阅读：MDNet: Learning Multi-Domain Convolutional Neural Networks for Visual Tracking
10. Learning Multi-Domain Convolutional Neural Networks for Visual Tracking CVPR 2016 阅读笔记

>>更多相关文章<<