To deal with the great number of untrimmed videos produced every day, we propose an
efficient unsupervised action segmentation method by detecting boundaries, named action …

We tackle real-world long-horizon robot manipulation tasks through skill discovery. We
present a bottom-up approach to learning a library of reusable skills from unsegmented …

This paper addresses a new problem of weakly-supervised online action segmentation in
instructional videos. We present a framework to segment streaming videos online at test time …

This work describes an end-to-end approach for real-time human action recognition from
raw depth image-sequences. The proposal is based on a 3D fully convolutional neural …

Temporal action segmentation is a task to classify each frame in the video with an action
label. However, it is quite expensive to annotate every frame in a large corpus of videos to …

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 …

Our lives can be seen as a complex weaving of activities; we switch from one activity to
another, to maximise our achievements or in reaction to demands placed upon us …

We present a semi-supervised learning approach to the temporal action segmentation task.
The goal of the task is to temporally detect and segment actions in long, untrimmed …

Using neural networks to build a reliable fault detection model is an attractive topic in
industrial processes but remains challenging due to the lack of labeled data. We propose a …

We propose a novel video sampling scheme for human action recognition in videos, using
Gaussian Weighing Function. Traditionally in deep learning-based human activity …