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

Listwise View Ranking for Image Cropping