collaborators

6 papers

q-bio.GN2025

A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100

Muhammad Omer Latif, Hayat Ullah, Muhammad Ali Shafique +1

Epilepsy is a chronic neurological condition characterized by recurrent seizures, with global prevalence estimated at 50 million people worldwide. While progress in high-throughput…

cs.CV2025

Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation

Hayat Ullah, Syed Muhammad Talha Zaidi, Arslan Munir

Convolutional neural networks (CNNs) excel in computer vision but are susceptible to adversarial attacks, crafted perturbations designed to mislead predictions. Despite advances in…

cs.DC2025

Cooling Matters: Benchmarking Large Language Models and Vision-Language Models on Liquid-Cooled Versus Air-Cooled H100 GPU Systems

Imran Latif, Muhammad Ali Shafique, Hayat Ullah +3

The unprecedented growth in artificial intelligence (AI) workloads, recently dominated by large language models (LLMs) and vision-language models (VLMs), has intensified power and…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…