  Supervised deep learning techniques have achieved great success in various
fields due to getting rid of the limitation of handcrafted representations.
However, most previous image retargeting algorithms still employ fixed design
principles such as using gradient map or handcrafted features to compute
saliency map, which inevitably restricts its generality. Deep learning
techniques may help to address this issue, but the challenging problem is that
we need to build a large-scale image retargeting dataset for the training of
deep retargeting models. However, building such a dataset requires huge human
efforts.
  In this paper, we propose a novel deep cyclic image retargeting approach,
called Cycle-IR, to firstly implement image retargeting with a single deep
model, without relying on any explicit user annotations. Our idea is built on
the reverse mapping from the retargeted images to the given input images. If
the retargeted image has serious distortion or excessive loss of important
visual information, the reverse mapping is unlikely to restore the input image
well. We constrain this forward-reverse consistency by introducing a cyclic
perception coherence loss. In addition, we propose a simple yet effective image
retargeting network (IRNet) to implement the image retargeting process. Our
IRNet contains a spatial and channel attention layer, which is able to
discriminate visually important regions of input images effectively, especially
in cluttered images. Given arbitrary sizes of input images and desired aspect
ratios, our Cycle-IR can produce visually pleasing target images directly.
Extensive experiments on the standard RetargetMe dataset show the superiority
of our Cycle-IR. In addition, our Cycle-IR outperforms the Multiop method and
obtains the best result in the user study. Code is available at
https://github.com/mintanwei/Cycle-IR.
