Visual tracking systems heavily rely on labeled data, posing challenges like high costs, scalability issues, and limited applicability in real-world environments.
Self-supervised learning (SSL) methods leverage the data itself to generate supervisory signals, reducing the dependence on labeled data for training models like AMDIM for visual tracking.
#visual-tracking-systems #self-supervised-learning #labeled-data #ssl-techniques #challenges-in-data-labeling
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