We introduce pix2gestalt, a framework for zero-shot amodal segmentation, which learns to
estimate the shape and appearance of whole objects that are only partially visible behind …

The research explores the utilization of a deep learning model employing an attention
mechanism in medical text mining. It targets the challenge of analyzing unstructured text …

Human posture detection is a rapidly evolving field with significant implications for various
applications, including healthcare, surveillance, and human-computer interaction. The …

This paper introduces an innovative multi-modal fusion deep learning approach to
overcome the drawbacks of traditional single-modal recognition techniques. These …

We propose RoHM an approach for robust 3D human motion reconstruction from monocular
RGB (-D) videos in the presence of noise and occlusions. Most previous approaches either …

With the advancement of artificial intelligence, 3D human pose estimation-based systems for
sports training and posture correction have gained significant attention in adolescent sports …

In this research, we introduce an innovative method for synthesizing medical images using
generative adversarial networks (GANs). Our proposed GANs method demonstrates the …

While current methods have shown promising progress on estimating 3D human motion
from monocular videos their motion estimates are often physically unrealistic because they …

Human pose forecasting garners attention for its diverse applications. However challenges
in modeling the multi-modal nature of human motion and intricate interactions among agents …

Human vision demonstrates higher robustness than current AI algorithms under out-of-
distribution scenarios. It has been conjectured such robustness benefits from performing …