Listwise View Ranking for Image Cropping
This work addresses image cropping for computer vision applications, offering an incremental improvement by combining listwise ranking with refined sampling.
The paper tackles the poor performance of ranking-based methods in image cropping by formulating it as a listwise ranking problem and proposing a refined view sampling technique (RoIRefine) to improve composition learning, achieving state-of-the-art results in both accuracy and speed.
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.