Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Within a large range of applications in computer vision, Human Action Recognition has
become one of the most attractive research fields. Ambiguities in recognizing actions does …

Research on depth-based human activity analysis achieved outstanding performance and
demonstrated the effectiveness of 3D representation for action recognition. The existing …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

Abstract Human-Object Interaction (HOI) detection lies at the core of action understanding.
Besides 2D information such as human/object appearance and locations, 3D pose is also …

We address the problem of image segmentation from natural language descriptions.
Existing deep learning-based methods encode image representations based on the output …

Human pose estimation and action recognition are related tasks since both problems are
strongly dependent on the human body representation and analysis. Nonetheless, most …

We propose a method for human activity recognition from RGB data that does not rely on
any pose information during test time, and does not explicitly calculate pose information …

Although synthetic training data has been shown to be beneficial for tasks such as human
pose estimation, its use for RGB human action recognition is relatively unexplored. Our goal …

We propose DeepGRU, a novel end-to-end deep network model informed by recent
developments in deep learning for gesture and action recognition, that is streamlined and …