  We address the problem of capturing temporal information for video
classification in 2D networks, without increasing their computational cost.
Existing approaches focus on modifying the architecture of 2D networks (e.g. by
including filters in the temporal dimension to turn them into 3D networks, or
using optical flow, etc.), which increases computation cost. Instead, we
propose a novel sampling strategy, where we re-order the channels of the input
video, to capture short-term frame-to-frame changes. We observe that without
bells and whistles, the proposed sampling strategy improves performance on
multiple architectures (e.g. TSN, TRN, TSM, and MVFNet) and datasets (CATER,
Something-Something-V1 and V2), up to 24% over the baseline of using the
standard video input. In addition, our sampling strategies do not require
training from scratch and do not increase the computational cost of training
and testing. Given the generality of the results and the flexibility of the
approach, we hope this can be widely useful to the video understanding
community. Code is available on our website:
https://github.com/kiyoon/channel_sampling.
