  Action segmentation as a milestone towards building automatic systems to
understand untrimmed videos has received considerable attention in the recent
years. It is typically being modeled as a sequence labeling problem but
contains intrinsic and sufficient differences than text parsing or speech
processing. In this paper, we introduce a novel hybrid temporal convolutional
and recurrent network (TricorNet), which has an encoder-decoder architecture:
the encoder consists of a hierarchy of temporal convolutional kernels that
capture the local motion changes of different actions; the decoder is a
hierarchy of recurrent neural networks that are able to learn and memorize
long-term action dependencies after the encoding stage. Our model is simple but
extremely effective in terms of video sequence labeling. The experimental
results on three public action segmentation datasets have shown that the
proposed model achieves superior performance over the state of the art.
