Deep Light-field-driven Saliency Detection from a Single View

Abstract

Previous 2D saliency detection methods extract salient cues from a single view and directly predict the expected results. Both traditional and deep-learning-based 2D methods do not consider geometric information of 3D scenes. Therefore the relationship between scene understanding and salient objects cannot be effectively established. This limits the performance of 2D saliency detection in challenging scenes. In this paper, we show for the first time that saliency detection problem can be reformulated as two sub-problems: light field synthesis from a single view and light-field-driven saliency detection. We propose a high-quality light field synthesis network to produce reliable 4D light field information. Then we propose a novel light-field-driven saliency detection network with two purposes, that is, i) richer saliency features can be produced for effective saliency detection; ii) geometric information can be considered for integration of multi-view saliency maps in a view-wise attention fashion. The whole pipeline can be trained in an end-to-end fashion. For training our network, we introduce the largest light field dataset for saliency detection, containing 1580 light fields that cover a wide variety of challenging scenes. With this new formulation, our method is able to achieve state-of-the-art performance.