3 accepted papers
"In temporal action segmentation, Timestamp supervision requires only a handful of labeled frames per video sequence. For unlabelled frames, Timestamp works rely on assigning hard labels and performance rapidly collapses under subtle violations of the annotation assumptions. We propose a novel Expec…
Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method for temporal action segmentation. Our method hinges on unsupervised representation learning, which, for temporal action…
Deep Learning methods are known to suffer from calibration issues: they typically produce over-confident estimates. These problems are exacerbated in the low data regime. Although the calibration of probabilistic models is well studied, calibrating extremely over-parametrized models in the low-data…