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Fully Convolutional Online Tracking
[article]
2021
arXiv
pre-print
Online learning has turned out to be effective for improving tracking performance. However, it could be simply applied for classification branch, but still remains challenging to adapt to regression branch due to its complex design and intrinsic requirement for high-quality online samples. To tackle this issue, we present the fully convolutional online tracking framework, coined as FCOT, and focus on enabling online learning for both classification and regression branches by using a target
arXiv:2004.07109v5
fatcat:lxwkgvz73vejrnjbgn6ydus4cu