72 citations
- Texas A&M UniversityUS8 papers
- Khalifa University of Science and TechnologyAE5 papers
- University of SharjahAE5 papers
- McGill UniversityCA4 papers
- Astronomical Observatory of CapodimonteIT2 papers
- Department of SpaceIN2 papers
- Johannes Gutenberg University MainzDE2 papers
- Max Planck Institute for AstronomyDE2 papers
- Mitchell InstituteUS2 papers
- Royal ObservatoryGB2 papers
- Stanford UniversityUS2 papers
- Université de MontréalCA2 papers
Showing 2024 · cs.CVShow all
3 papers · 2 filters
cs.CV2024★ 8 cited
Towards Real Zero-Shot Camouflaged Object Segmentation without Camouflaged Annotations
Cheng Lei, Jie Fan, Xinran Li +4
Camouflaged Object Segmentation (COS) faces significant challenges due to the scarcity of annotated data, where meticulous pixel-level annotation is both labor-intensive and costly…
cs.CV2024★ 1 cited
Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures
Mohammad Belal, Taimur Hassan, Abdelfatah Hassan +3
Human activity recognition is a major field of study that employs computer vision, machine vision, and deep learning techniques to categorize human actions. The field of deep learn…
cs.CV2024
Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models
Mohammad Belal, Taimur Hassan, Abdelfatah Ahmed +3
Human activity recognition (HAR) is a crucial area of research that involves understanding human movements using computer and machine vision technology. Deep learning has emerged a…