Continuous growth in software, hardware and internet technology has enabled the growth of
internet-based sensor tools that provide physical world observations and data …

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect
and/or generate various sensory data over time for a wide range of fields and applications …

Temporal action segmentation is crucial for understanding long-form videos. Previous works
on this task commonly adopt an iterative refinement paradigm by using multi-stage models …

Algorithms for the action segmentation task typically use temporal models to predict what
action is occurring at each frame for a minute-long daily activity. Recent studies have shown …

Temporally locating and classifying action segments in long untrimmed videos is of
particular interest to many applications like surveillance and robotics. While traditional …

Most video super-resolution methods super-resolve a single reference frame with the help of
neighboring frames in a temporal sliding window. They are less efficient compared to the …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …

We propose TAL-Net, an improved approach to temporal action localization in video that is
inspired by the Faster R-CNN object detection framework. TAL-Net addresses three key …

Temporally localizing actions in a video is a fundamental challenge in video understanding.
Most existing approaches have often drawn inspiration from image object detection and …

Detecting actions in untrimmed videos is an important yet challenging task. In this paper, we
present the structured segment network (SSN), a novel framework which models the …