Considering the significant progress made on RGB-based deep salient object detection (SOD) methods, this paper seeks to bridge the gap between those 2D methods and 4D light field data, instead of implementing specific 4D methods. We observe that the performance of 2D methods changes dramatically with the input refocusing on different depths. This paper attempts to make the 2D methods available for light field SOD by learning to select the best single image from the 4D tensor. Given a 2D method, a deep model is proposed to explicitly compare pairs of SOD results on one light field sample. Moreover, a comparator module is designed to integrate the features from a pair, which provides more discriminative representations to classify. Experiments over 13 latest 2D methods and 2 datasets demonstrate the proposed method can bring about 24.0% and 5.3% average improvement of mean absolute error and F-measure, and outperform state-of-the-art 4D methods by a large margin.

考虑到基于RGB的深显着物体检测（SOD）方法取得的重大进展，本文力图弥合那些2D方法和4D光场数据之间的差距，而不是实施特定的4D方法。我们观察到2D方法的性能会随着输入重新聚焦于不同深度而发生巨大变化。本文试图通过学习从4D张量中选择最佳的单个图像，使2D方法可用于光场SOD。给定一种2D方法，提出了一个深层模型来明确比较一个光场样本上的SOD结果对。此外，设计了一个比较器模块以集成一对特征，从而提供了更具区分性的分类表示。在13种最新的2D方法和2个数据集上进行的实验表明，该方法可以带来24.0平均绝对误差和F测度的平均改善幅度为％和5.3 ％，并且大大超过了最新的4D方法。