SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots. (arXiv:2011.06252v2 [cs.CV] UPDATED)

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 …

arxiv attention autonomous cv modeling robots visual attention