Search Results for author:
Found 5 papers, 2 papers with code
Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units
The study demonstrates the utility of our deep representation learning-based approach in unraveling phenotypes that reflect the heterogeneity in sepsis-induced ARF in terms of different mortality outcomes and severity.
A Modular Multi-stage Lightweight Graph Transformer Network for Human Pose and Shape Estimation from 2D Human Pose
In this research, we address the challenge faced by existing deep learning-based human mesh reconstruction methods in balancing accuracy and computational efficiency.
Skeleton-based Human Action Recognition via Convolutional Neural Networks (CNN)
Recently, there has been a remarkable increase in the interest towards skeleton-based action recognition within the research community, owing to its various advantageous features, including computational efficiency, representative features, and illumination invariance.
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals.
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities.
Ranked #6 on
Multiview Gait Recognition
on CASIA-B
