  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 effectively capture long-range
dependency by self-attention mechanism, while having the limitation on reducing
local redundancy with blind similarity comparison among all the tokens in each
layer. Based on these observations, we propose a novel Unified transFormer
(UniFormer) which seamlessly integrates merits of 3D convolution and
spatiotemporal self-attention in a concise transformer format, and achieves a
preferable balance between computation and accuracy. Different from traditional
transformers, our relation aggregator can tackle both spatiotemporal redundancy
and dependency, by learning local and global token affinity respectively in
shallow and deep layers. We conduct extensive experiments on the popular video
benchmarks, e.g., Kinetics-400, Kinetics-600, and Something-Something V1&V2.
With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1
accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than
other state-of-the-art methods. For Something-Something V1 and V2, our
UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1
accuracy respectively. Code is available at
https://github.com/Sense-X/UniFormer.
