Papers › Object Detection with Deep Reinforcement Learning

Object Detection with Deep Reinforcement Learning

9 Aug 2022arXiv:2208.04511archive 2025-07-28

Manoosh Samiei, Ruofeng Li

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.

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Active Object Localization Deep Reinforcement Learning Object Object Detection Object Localization Reinforcement Learning Reinforcement Learning (RL)object-detection reinforcement-learning

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