37 citations · 113 across the 22 of their papers we have counts for
24 papers
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
Hayat Ullah, Muhammad Ali Shafique, Abbas Khan +1
The landscape of video recognition has evolved significantly, shifting from traditional Convolutional Neural Networks (CNNs) to Transformer-based architectures for improved accurac…
OD-VIRAT: A Large-Scale Benchmark for Object Detection in Realistic Surveillance Environments
Hayat Ullah, Abbas Khan, Arslan Munir +1
Realistic human surveillance datasets are crucial for training and evaluating computer vision models under real-world conditions, facilitating the development of robust algorithms…
Hierarchical Multi-Stage Transformer Architecture for Context-Aware Temporal Action Localization
Hayat Ullah, Arslan Munir, Oliver Nina
Inspired by the recent success of transformers and multi-stage architectures in video recognition and object detection domains. We thoroughly explore the rich spatio-temporal prope…
ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos
James Wensel, Hayat Ullah, Arslan Munir
Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing. The appl…
Human Activity Recognition Using Cascaded Dual Attention CNN and Bi-Directional GRU Framework
Hayat Ullah, Arslan Munir
Vision-based human activity recognition has emerged as one of the essential research areas in video analytics domain. Over the last decade, numerous advanced deep learning algorith…
Phantom: A High-Performance Computational Core for Sparse Convolutional Neural Networks
Mahmood Azhar Qureshi, Arslan Munir
Sparse convolutional neural networks (CNNs) have gained significant traction over the past few years as sparse CNNs can drastically decrease the model size and computations, if exp…