  This paper presents A3D, an adaptive 3D network that can infer at a wide
range of computational constraints with one-time training. Instead of training
multiple models in a grid-search manner, it generates good configurations by
trading off between network width and spatio-temporal resolution. Furthermore,
the computation cost can be adapted after the model is deployed to meet
variable constraints, for example, on edge devices. Even under the same
computational constraints, the performance of our adaptive networks can be
significantly boosted over the baseline counterparts by the mutual training
along three dimensions. When a multiple pathway framework, e.g. SlowFast, is
adopted, our adaptive method encourages a better trade-off between pathways
than manual designs. Extensive experiments on the Kinetics dataset show the
effectiveness of the proposed framework. The performance gain is also verified
to transfer well between datasets and tasks. Code will be made available.
