  This paper presents a holistic approach to saliency-guided visual attention
modeling (SVAM) for use by autonomous underwater robots. Our proposed model,
named SVAM-Net, integrates deep visual features at various scales and semantics
for effective salient object detection (SOD) in natural underwater images. The
SVAM-Net architecture is configured in a unique way to jointly accommodate
bottom-up and top-down learning within two separate branches of the network
while sharing the same encoding layers. We design dedicated spatial attention
modules (SAMs) along these learning pathways to exploit the coarse-level and
fine-level semantic features for SOD at four stages of abstractions. The
bottom-up branch performs a rough yet reasonably accurate saliency estimation
at a fast rate, whereas the deeper top-down branch incorporates a residual
refinement module (RRM) that provides fine-grained localization of the salient
objects. Extensive performance evaluation of SVAM-Net on benchmark datasets
clearly demonstrates its effectiveness for underwater SOD. We also validate its
generalization performance by several ocean trials' data that include test
images of diverse underwater scenes and waterbodies, and also images with
unseen natural objects. Moreover, we analyze its computational feasibility for
robotic deployments and demonstrate its utility in several important use cases
of visual attention modeling.
