Robust Tracking using Manifold Convolutional Neural Networks with Laplacian Regularization
In visual tracking, usually only a small number of samples are labeled, and most existing deep learning based trackers ignore abundant unlabeled samples that could provide additional information for deep trackers to boost their tracking performance. An intuitive way to explain unlabeled data is to incorporate manifold regularization into the common classification loss functions, but the high computational cost may prohibit those deep trackers from practical applications. To overcome this issue,...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Multimedia|
|Access Rights:||Open Access|
|01_Hu_Robust_Tracking_using_Manifold_2018.pdf||4.63 MB||Adobe PDF|
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