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Jan, 2022
一种深度Q学习方法用于优化动态噪声背景下的视觉搜索策略
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise
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Weimin Zhou, Miguel P. Eckstein
TL;DR
本文研究了应用强化学习方法(Q-network)近似理想搜索者(IS)的眼动规划能力,并发现相应的搜索策略与理想搜索者的搜索策略一致,展示了该方法在实际医学图像场景中测量优化眼动规划的潜力。
Abstract
Humans process visual information with varying resolution (
foveated visual system
) and explore images by orienting through eye movements the high-resolution fovea to points of interest. The
bayesian ideal searcher
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