Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning

CVPR 2018 Xingping DongJianbing ShenWenguan WangYu LiuLing ShaoFatih Porikli

Hyperparameters are numerical presets whose values are assigned prior to the commencement of the learning process. Selecting appropriate hyperparameters is critical for the accuracy of tracking algorithms, yet it is difficult to determine their optimal values, in particular, adaptive ones for each specific video sequence... (read more)

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