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This paper introduces a unified framework for video action segmentation viasequence to sequence (seq2seq) translation in a fully and timestamp supervisedsetup. In contrast to current state-of-the-art frame-level prediction methods,we view action segmentation as a seq2seq translation task, i.e., mapping asequence of video frames to a sequence of action segments. Our proposed methodinvolves a series of modifications and auxiliary loss functions on the standardTransformer seq2seq translation model to cope with long input sequences opposedto short output sequences and relatively few videos. We incorporate anauxiliary supervision signal for the encoder via a frame-wise loss and proposea separate alignment decoder for an implicit duration prediction. Finally, weextend our framework to the timestamp supervised setting via our proposedconstrained k-medoids algorithm to generate pseudo-segmentations. Our proposedframework performs consistently on both fully and timestamp supervisedsettings, outperforming or competing state-of-the-art on several datasets. Ourcode is publicly available at https://github.com/boschresearch/UVAST.

Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation