Classification of human actions is an ongoing research problem in computer vision. This
review is aimed to scope current literature on data fusion and action recognition techniques …

Temporal action detection is a fundamental yet challenging task in video understanding.
Video context is a critical cue to effectively detect actions, but current works mainly focus on …

Abstract Spatial-temporal, channel-wise, and motion patterns are three complementary and
crucial types of information for video action recognition. Conventional 2D CNNs are …

Video data is with complex temporal dynamics due to various factors such as camera
motion, speed variation, and different activities. To effectively capture this diverse motion …

Understanding dynamic 3D environment is crucial for robotic agents and many other
applications. We propose a novel neural network architecture called MeteorNet for learning …

Unsupervised learning has been popular in various computer vision tasks, including visual
object tracking. However, prior unsupervised tracking approaches rely heavily on spatial …

Motion plays a crucial role in understanding videos and most state-of-the-art neural models
for video classification incorporate motion information typically using optical flows extracted …

Motion, as the uniqueness of a video, has been critical to the development of video
understanding models. Modern deep learning models leverage motion by either executing …

Over the past several years, we have witnessed impressive progress in the field of learned
image compression. Recent learned image codecs are commonly based on autoencoders …

This paper presents a new task named weakly-supervised group activity recognition (GAR)
which differs from conventional GAR tasks in that only video-level labels are available, yet …