Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action Recognition. (arXiv:2201.10394v1 [cs.CV])

We address the problem of capturing temporal information for video
classification in 2D networks, without increasing 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 …

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