  Light field saliency detection -- important due to utility in many vision
tasks -- still lacks speed and can improve in accuracy. Due to the formulation
of the saliency detection problem in light fields as a segmentation task or a
memorizing task, existing approaches consume unnecessarily large amounts of
computational resources for training, and have longer execution times for
testing. We solve this by aggressively reducing the large light field images to
a much smaller three-channel feature map appropriate for saliency detection
using an RGB image saliency detector with attention mechanisms. We achieve this
by introducing a novel convolutional neural network based features extraction
and encoding module. Our saliency detector takes $0.4$ s to process a light
field of size $9\times9\times512\times375$ in a CPU and is significantly faster
than state-of-the-art light field saliency detectors, with better or comparable
accuracy. Furthermore, model size of our architecture is significantly lower
compared to state-of-the-art light field saliency detectors. Our work shows
that extracting features from light fields through aggressive size reduction
and the attention mechanism results in a faster and accurate light field
saliency detector leading to near real-time light field processing.
