“Temporal Convolutional Networks: A Unified Approach to Action Segmentation”, Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager2016-08-29 (CNN, video analysis; backlinks)⁠:

The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Neural Network that encode spatiotemporal information locally, and second, input these features into a classifier that captures high-level temporal relationships, such as a Recurrent Neural Network (RNN). While often effective, this decoupling requires specifying two separate models, each with their own complexities, and prevents capturing more nuanced long-range spatiotemporal relationships.

We propose a unified approach, as demonstrated by our Temporal Convolutional Network (TCN), that hierarchically captures relationships at low/intermediate-/high-level time-scales.

Our model achieves superior or competitive performance using video or sensor data on 3 public action segmentation datasets and can be trained in a fraction of the time it takes to train an RNN.

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