  Rank-based Learning with deep neural network has been widely used for image
cropping. However, the performance of ranking-based methods is often poor and
this is mainly due to two reasons: 1) image cropping is a listwise ranking task
rather than pairwise comparison; 2) the rescaling caused by pooling layer and
the deformation in view generation damage the performance of composition
learning. In this paper, we develop a novel model to overcome these problems.
To address the first problem, we formulate the image cropping as a listwise
ranking problem to find the best view composition. For the second problem, a
refined view sampling (called RoIRefine) is proposed to extract refined feature
maps for candidate view generation. Given a series of candidate views, the
proposed model learns the Top-1 probability distribution of views and picks up
the best one. By integrating refined sampling and listwise ranking, the
proposed network called LVRN achieves the state-of-the-art performance both in
accuracy and speed.
