Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
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. Most hyperparameter optimization algorithms depend on searching a generic range and they are imposed blindly on all sequences. Here, we propose a novel hyperparameter...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Book Title:||31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018|
|01_Dong_Hyperparameter_Optimization_2018.pdf||948.25 kB||Adobe PDF|
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