  To capture high-speed videos using a two-dimensional detector, video snapshot
compressive imaging (SCI) is a promising system, where the video frames are
coded by different masks and then compressed to a snapshot measurement.
Following this, efficient algorithms are desired to reconstruct the high-speed
frames, where the state-of-the-art results are achieved by deep learning
networks. However, these networks are usually trained for specific small-scale
masks and often have high demands of training time and GPU memory, which are
hence {\bf \em not flexible} to $i$) a new mask with the same size and $ii$) a
larger-scale mask. We address these challenges by developing a Meta Modulated
Convolutional Network for SCI reconstruction, dubbed MetaSCI. MetaSCI is
composed of a shared backbone for different masks, and light-weight
meta-modulation parameters to evolve to different modulation parameters for
each mask, thus having the properties of {\bf \em fast adaptation} to new masks
(or systems) and ready to {\bf \em scale to large data}. Extensive simulation
and real data results demonstrate the superior performance of our proposed
approach. Our code is available at
{\small\url{https://github.com/xyvirtualgroup/MetaSCI-CVPR2021}}.
