Spatiotemporal human representation based on 3D visual perception data is a rapidly
growing research area. Representations can be broadly categorized into two groups …

Abstract 3D action recognition–analysis of human actions based on 3D skeleton data–
becomes popular recently due to its succinctness, robustness, and view-invariant …

Abstract Long Short-Term Memory (LSTM) networks have shown superior performance in
3D human action recognition due to their power in modeling the dynamics and …

Skeleton-based human action recognition has attracted a lot of research attention during the
past few years. Recent works attempted to utilize recurrent neural networks to model the …

In this work, we address human activity and hand gesture recognition problems using 3D
data sequences obtained from full-body and hand skeletons, respectively. To this aim, we …

Human action recognition in 3D skeleton sequences has attracted a lot of research attention.
Recently, long short-term memory (LSTM) networks have shown promising performance in …

Human actions comprise of joint motion of articulated body parts orgestures'. Human
skeleton is intuitively represented as a sparse graph with joints as nodes and natural …

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

Recent studies demonstrate the effectiveness of Recurrent Neural Networks (RNNs) for
action recognition in videos. However, previous works mainly utilize video-level category as …

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 …