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点击上方 “机器学习研究会” 可以订阅哦 摘要 转自：视觉机器人 CVPR 2017论文《Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning》摘要： This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale. The deep network to control actions is pre-trained using various training sequences and fine-tuned during tracking for online adaptation to target and background changes. The pre-training is done by utilizing deep reinforcement learning as well as supervised learning. The use of reinforcement learning enables even partially labeled data to be successfully utiliz
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