  Spatial convolutions are widely used in numerous deep video models. It
fundamentally assumes spatio-temporal invariance, i.e., using shared weights
for every location in different frames. This work presents Temporally-Adaptive
Convolutions (TAdaConv) for video understanding, which shows that adaptive
weight calibration along the temporal dimension is an efficient way to
facilitate modelling complex temporal dynamics in videos. Specifically,
TAdaConv empowers the spatial convolutions with temporal modelling abilities by
calibrating the convolution weights for each frame according to its local and
global temporal context. Compared to previous temporal modelling operations,
TAdaConv is more efficient as it operates over the convolution kernels instead
of the features, whose dimension is an order of magnitude smaller than the
spatial resolutions. Further, the kernel calibration brings an increased model
capacity. We construct TAda2D and TAdaConvNeXt networks by replacing the 2D
convolutions in ResNet and ConvNeXt with TAdaConv, which leads to at least on
par or better performance compared to state-of-the-art approaches on multiple
video action recognition and localization benchmarks. We also demonstrate that
as a readily plug-in operation with negligible computation overhead, TAdaConv
can effectively improve many existing video models with a convincing margin.
