  Light field data exhibit favorable characteristics conducive to saliency
detection. The success of learning-based light field saliency detection is
heavily dependent on how a comprehensive dataset can be constructed for higher
generalizability of models, how high dimensional light field data can be
effectively exploited, and how a flexible model can be designed to achieve
versatility for desktop computers and mobile devices. To answer these
questions, first we introduce a large-scale dataset to enable versatile
applications for RGB, RGB-D and light field saliency detection, containing 102
classes and 4204 samples. Second, we present an asymmetrical two-stream model
consisting of the Focal stream and RGB stream. The Focal stream is designed to
achieve higher performance on desktop computers and transfer focusness
knowledge to the RGB stream, relying on two tailor-made modules. The RGB stream
guarantees the flexibility and memory/computation efficiency on mobile devices
through three distillation schemes. Experiments demonstrate that our Focal
stream achieves state-of-the-arts performance. The RGB stream achieves Top-2
F-measure on DUTLF-V2, which tremendously minimizes the model size by 83% and
boosts FPS by 5 times, compared with the best performing method. Furthermore,
our proposed distillation schemes are applicable to RGB saliency models,
achieving impressive performance gains while ensuring flexibility.
