  Dynamic Time Warping (DTW) is widely used for temporal data processing.
However, existing methods can neither learn the discriminative prototypes of
different classes nor exploit such prototypes for further analysis. We propose
Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific
discriminative prototypes for temporal recognition tasks. DP-DTW shows superior
performance compared to conventional DTWs on time series classification
benchmarks. Combined with end-to-end deep learning, DP-DTW can handle
challenging weakly supervised action segmentation problems and achieves state
of the art results on standard benchmarks. Moreover, detailed reasoning on the
input video is enabled by the learned action prototypes. Specifically, an
action-based video summarization can be obtained by aligning the input sequence
with action prototypes.
