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• W3196789904 abstract "State-of-the-art object detection models are frequently trained offline using available datasets, such as ImageNet: large and overly diverse data that are unbalanced and hard to cluster semantically. This kind of training drops the object detection performance should the change in illumination, in the environmental conditions (e.g., rain), or in the lens positioning (out-of-focus blur) occur. We propose a decentralized hierarchical multi-agent deep reinforcement learning approach for intelligently controlling the camera and the lens focusing settings, leading to significant improvement to the capacity of the popular detection models (YOLO, Fast R-CNN, and Retina are considered). The algorithm relies on the latent representation of the camera's stream and, thus, it is the first method to allow a completely no-reference tuning of the camera, where the system trains itself to auto-focus itself." @default.
• W3196789904 created "2021-09-13" @default.
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• W3196789904 date "2021-08-29" @default.
• W3196789904 modified "2023-09-23" @default.
• W3196789904 title "Decentralized Autofocusing System with Hierarchical Agents" @default.
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