  The ability of snapshot compressive imaging (SCI) systems to efficiently
capture high-dimensional (HD) data depends on the advent of novel optical
designs to sample the HD data as two-dimensional (2D) compressed measurements.
Nonetheless, the traditional SCI scheme is fundamentally limited, due to the
complete disregard for high-level information in the sampling process. To
tackle this issue, in this paper, we pave the first mile toward the advanced
design of adaptive coding masks for SCI. Specifically, we propose an efficient
and effective algorithm to generate coding masks with the assistance of
saliency detection, in a low-cost and low-power fashion. Experiments
demonstrate the effectiveness and efficiency of our approach. Code is available
at: https://github.com/IndigoPurple/SASA
