Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning. (arXiv:2201.04676v1 [cs.CV])

It is a challenging task to learn rich and multi-scale spatiotemporal
semantics from high-dimensional videos, due to large local redundancy and
complex global dependency between video frames. The recent advances in this
research have been mainly driven by 3D convolutional neural networks and vision
transformers. Although 3D convolution can efficiently aggregate local context
to suppress local redundancy from a small 3D neighborhood, it lacks the
capability to capture global dependency because of the limited receptive field.
Alternatively, vision transformers can …

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