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- Texas A&M UniversityUS8 papers
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4 papers · 1 filter
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…
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…
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…
Effects of Strong Capacitive Coupling Between Meta-Atoms in rf SQUID Metamaterials
Jingnan Cai, Robin Cantor, Johanne Hizanidis +2
We consider, for the first time, the effects of strong capacitive and inductive coupling between radio frequency Superconducting Quantum Interference Devices (rf SQUIDs) in an over…