Abstract
Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly
supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding
window mechanism requires fixed aspect ratios and limits
the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of
windows on the input image which is very time-consuming.
Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this
problem. Particularly, the proposed method develops an
aesthetics aware reward function which especially bene-
fits image cropping. Similar to human’s decision making,
we use a comprehensive state representation including both
the current observation and the historical experience. We
train the agent using the actor-critic architecture in an endto-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that
our method achieves the state-of-the-art performance with
much fewer candidate windows and much less time compared with previous weakly supervised methods.