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

Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning