# Listwise View Ranking for Image Cropping

## TL;DR

This paper introduces a novel listwise ranking model with refined view sampling for image cropping, significantly improving accuracy and speed over previous pairwise methods by better capturing view composition and reducing deformation effects.

## Contribution

The paper proposes a listwise ranking approach combined with RoIRefine sampling to enhance image cropping performance, addressing key limitations of existing ranking-based methods.

## Key findings

- Achieves state-of-the-art accuracy in image cropping
- Improves speed compared to previous methods
- Effectively models view composition with refined sampling

## Abstract

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.