论文标题
深入增强学习的对象检测
Object Detection with Deep Reinforcement Learning
论文作者
论文摘要
在计算机视野字段中，对象定位一直是至关重要的任务。已经根据参加像素的特征提出了在图像中定位对象的方法。最近，研究人员提出了将对象定位作为动态决策过程的方法，可以通过增强学习方法来解决。在这个项目中，我们基于深度强化学习实现了一种新颖的活动对象本地化算法。我们比较了此MDP的两个不同的动作设置：一种分层方法和动态方法。我们通过研究不同的超参数和各种体系结构的变化，进一步对模型的性能进行一些消融研究。
Object localization has been a crucial task in computer vision field. Methods of localizing objects in an image have been proposed based on the features of the attended pixels. Recently researchers have proposed methods to formulate object localization as a dynamic decision process, which can be solved by a reinforcement learning approach. In this project, we implement a novel active object localization algorithm based on deep reinforcement learning. We compare two different action settings for this MDP: a hierarchical method and a dynamic method. We further perform some ablation studies on the performance of the models by investigating different hyperparameters and various architecture changes.
