A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

TL;DR

This paper introduces A2-RL, a reinforcement learning framework for image cropping that models the task as a sequential decision process, improving efficiency and aesthetic quality without bounding box supervision.

Contribution

It formulates aesthetic image cropping as a sequential decision-making problem and develops an aesthetics-aware reward function within a reinforcement learning framework.

Findings

Achieves state-of-the-art performance on unseen cropping datasets.

Uses fewer candidate windows and less computation time.

Outperforms previous weakly supervised methods.

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 benefits 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 end-to-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.