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Visual Tracking by Adaptive Continual Meta-Learning
2022
IEEE Access
We formulate the visual tracking problem as a semi-supervised continual learning problem, where only an initial frame is labeled. In contrast to conventional meta-learning based approaches that regard visual tracking as an instance detection problem with a focus on finding good weights for model initialization, we consider both initialization and online update processes simultaneously under our adaptive continual meta-learning framework. The proposed adaptive meta-learning strategy dynamically
doi:10.1109/access.2022.3143809
fatcat:ghc7qvhtafhohe26wzxblbbn7m