  In the past few years, numerous deep learning methods have been proposed to
address the task of segmenting salient objects from RGB images. However, these
approaches depending on single modality fail to achieve the state-of-the-art
performance on widely used light field salient object detection (SOD) datasets,
which collect large-scale natural images and provide multiple modalities such
as multi-view, micro-lens images and depth maps. Most recently proposed light
field SOD methods have acquired improving detecting accuracy, yet still predict
rough objects' structures and perform slow inference speed. To this end, we
propose CMA-Net, which consists of two novel cascaded mutual attention modules
aiming at fusing the high level features from the modalities of all-in-focus
and depth. Our proposed CMA-Net outperforms 30 SOD methods on two widely
applied light field benchmark datasets. Besides, the proposed CMA-Net is able
to inference at the speed of 53 fps. Extensive quantitative and qualitative
experiments illustrate both the effectiveness and efficiency of our CMA-Net.
