  Partially annotated clips contain rich temporal contexts that can complement
the sparse key frame annotations in providing supervision for model training.
We present a novel paradigm called Temporally-Adaptive Features (TAF) learning
that can utilize such data to learn better single frame models. By imposing
distinct temporal change rate constraints on different factors in the model,
TAF enables learning from unlabeled frames using context to enhance model
accuracy. TAF generalizes "slow feature" learning and we present much stronger
empirical evidence than prior works, showing convincing gains for the
challenging semantic segmentation task over a variety of architecture designs
and on two popular datasets. TAF can be interpreted as an implicit label
augmentation method but is a more principled formulation compared to existing
explicit augmentation techniques. Our work thus connects two promising methods
that utilize partially annotated clips for single frame model training and can
inspire future explorations in this direction.
