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Visual Tracking by Adaptive Continual Meta-Learning
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AI
DOI：10.1109/ACCESS.2022.3143809.png)
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摘要
En 中文
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 generates the hyperparameters needed for fast initialization and online update to achieve more robustness via adaptively regulating the learning process. In addition, our continual meta-learning approach based on knowledge distillation scheme helps the tracker adapt to new examples while retaining its knowledge on previously seen examples. We apply our proposed framework to deep learning-based tracking algorithm to obtain noticeable performance gains and competitive results against recent state-of-the-art tracking algorithms while performing at real-time speeds.
Keyword:
Visualization
Target tracking
Adaptation models
Training
Knowledge engineering
Classification algorithms
Task analysis
Continual learning
meta learning
object tracking
visual tracking
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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