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Action segmentation as a milestone towards building automatic systems tounderstand untrimmed videos has received considerable attention in the recentyears. It is typically being modeled as a sequence labeling problem butcontains intrinsic and sufficient differences than text parsing or speechprocessing. In this paper, we introduce a novel hybrid temporal convolutionaland recurrent network (TricorNet), which has an encoder-decoder architecture:the encoder consists of a hierarchy of temporal convolutional kernels thatcapture the local motion changes of different actions; the decoder is ahierarchy of recurrent neural networks that are able to learn and memorizelong-term action dependencies after the encoding stage. Our model is simple butextremely effective in terms of video sequence labeling. The experimentalresults on three public action segmentation datasets have shown that theproposed model achieves superior performance over the state of the art.

TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation