  We introduce a weakly supervised method for representation learning based on
aligning temporal sequences (e.g., videos) of the same process (e.g., human
action). The main idea is to use the global temporal ordering of latent
correspondences across sequence pairs as a supervisory signal. In particular,
we propose a loss based on scoring the optimal sequence alignment to train an
embedding network. Our loss is based on a novel probabilistic path finding view
of dynamic time warping (DTW) that contains the following three key features:
(i) the local path routing decisions are contrastive and differentiable, (ii)
pairwise distances are cast as probabilities that are contrastive as well, and
(iii) our formulation naturally admits a global cycle consistency loss that
verifies correspondences. For evaluation, we consider the tasks of fine-grained
action classification, few shot learning, and video synchronization. We report
significant performance increases over previous methods. In addition, we report
two applications of our temporal alignment framework, namely 3D pose
reconstruction and fine-grained audio/visual retrieval.
