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 …

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 …

Abstract 3D human pose estimation and mesh recovery have attracted widespread research
interest in many areas, such as computer vision, autonomous driving, and robotics. Deep …

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 …

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

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

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their
detection and classification essential. The prevalent use of transfer learning in recognition …

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 …