Research in human action recognition has accelerated significantly since the introduction of
powerful machine learning tools such as Convolutional Neural Networks (CNNs). However …

Two-stream network architecture has the ability to capture temporal and spatial features from
videos simultaneously and has achieved excellent performance on video action recognition …

Action recognition has been an active research area for many years. Extracting
discriminative spatial and temporal features of different actions plays a key role in …

Despite the success of deep learning in video understanding tasks, processing every frame
in a video is computationally expensive and often unnecessary in real-time applications …

Video action recognition needs to model any differences by subdividing the spatio-temporal
features to distinguish various actions. We propose rethinking spatio-temporal cross …

This paper introduces a fusion convolutional architecture for efficient learning of spatio-
temporal features in video action recognition. Unlike 2D convolutional neural networks …

Learning deep representations have been applied in action recognition widely. However,
there have been a few investigations on how to utilize the structural manifold information …

Visual tempo characterizes the dynamics and the temporal scale of an action. Modeling
such visual tempos of different actions facilitates their recognition. Previous works often …

While many action recognition datasets consist of collections of brief, trimmed videos each
containing a relevant action, videos in the real-world (eg, on YouTube) exhibit very different …

We address the problem of data augmentation for video action recognition. Standard
augmentation strategies in video are hand-designed and sample the space of possible …