247 research outputs found
Video Transformers: A Survey
Transformer models have shown great success handling long-range interactions,
making them a promising tool for modeling video. However they lack inductive
biases and scale quadratically with input length. These limitations are further
exacerbated when dealing with the high dimensionality introduced with the
temporal dimension. While there are surveys analyzing the advances of
Transformers for vision, none focus on an in-depth analysis of video-specific
designs. In this survey we analyze main contributions and trends of works
leveraging Transformers to model video. Specifically, we delve into how videos
are handled as input-level first. Then, we study the architectural changes made
to deal with video more efficiently, reduce redundancy, re-introduce useful
inductive biases, and capture long-term temporal dynamics. In addition we
provide an overview of different training regimes and explore effective
self-supervised learning strategies for video. Finally, we conduct a
performance comparison on the most common benchmark for Video Transformers
(i.e., action classification), finding them to outperform 3D ConvNets even with
less computational complexity
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