• Corpus ID: 226307119

SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots

@article{Islam2020SVAMSV,
  title={SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots},
  author={Md. Jahidul Islam and Ruobing Wang and Karin de Langis and Junaed Sattar},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.06252}
}
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… 
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