33,973 research outputs found
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
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.Comment: Accepted by CVPR 201
Practical Block-wise Neural Network Architecture Generation
Convolutional neural networks have gained a remarkable success in computer
vision. However, most usable network architectures are hand-crafted and usually
require expertise and elaborate design. In this paper, we provide a block-wise
network generation pipeline called BlockQNN which automatically builds
high-performance networks using the Q-Learning paradigm with epsilon-greedy
exploration strategy. The optimal network block is constructed by the learning
agent which is trained sequentially to choose component layers. We stack the
block to construct the whole auto-generated network. To accelerate the
generation process, we also propose a distributed asynchronous framework and an
early stop strategy. The block-wise generation brings unique advantages: (1) it
performs competitive results in comparison to the hand-crafted state-of-the-art
networks on image classification, additionally, the best network generated by
BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing
auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of
the search space in designing networks which only spends 3 days with 32 GPUs,
and (3) moreover, it has strong generalizability that the network built on
CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201
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