QUICKSAL A small and sparse visual saliency model for efficient inference in resource constrained hardware

QUICKSAL A small and sparse visual saliency model for efficient inference in resource constrained hardware

Visual saliency is an important problem in the field of cognitive science and computer vision with applications such as surveillance, adaptive compressing, detecting unknown objects, and scene understanding. In this paper, we propose a small and sparse neural network model for performing salient object segmentation that is suitable for use in mobile and embedded applications. Our model is built using depthwise separable convolutions and bottleneck inverted residuals which have been proven to perform very memory efficient inference and can be easily implemented using standard functions available in all deep learning frameworks.

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