  Data input modality plays an important role in video action recognition.
Normally, there are three types of input: RGB, flow stream and compressed data.
In this paper, we proposed a new input modality: gray stream. Specifically,
taken the stacked consecutive 3 gray images as input, which is the same size of
RGB, can not only skip the conversion process from video decoding data to RGB,
but also improve the spatio-temporal modeling ability at zero computation and
zero parameters. Meanwhile, we proposed a 1D Identity Channel-wise
Spatio-temporal Convolution(1D-ICSC) which captures the temporal relationship
at channel-feature level within a controllable computation budget(by parameters
G & R). Finally, we confirm its effectiveness and efficiency on several action
recognition benchmarks, such as Kinetics, Something-Something, HMDB-51 and
UCF-101, and achieve impressive results.
