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.

本文提出了一种新的混合时态卷积和循环神经网络 (TricorNet) 模型，其具有编码器 - 解码器结构，通过编码器捕捉不同动作的局部运动变化，解码器能够学习和记忆长期的动作依赖性，该模型在三个行动分割数据集上的实验结果表明，在视频序列标注方面，所提出的模型优于现有技术水平。