1 citations · 1 across the 13 of their papers we have counts for
11 papers · 1 filter
Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation
Ali Zia, Muhammad Umer Ramzan, Abdelwahed Khamis +2
Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements make dense semantic segmentation…
Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation
Ali Zia, Usman Ali, Abdul Rehman +5
Test-time adaptation (TTA) has emerged as a promising paradigm for mitigating distribution shifts in deep models. However, existing TTA approaches for anomaly segmentation remain l…
Geometry-Aware Semantic Reasoning for Training Free Video Anomaly Detection
Ali Zia, Usman Ali, Muhammad Umer Ramzan +3
Training-free video anomaly detection (VAD) has recently emerged as a scalable alternative to supervised approaches, yet existing methods largely rely on static prompting and geome…
Component-Aware Sketch-to-Image Generation Using Self-Attention Encoding and Coordinate-Preserving Fusion
Ali Zia, Muhammad Umer Ramzan, Usman Ali +3
Translating freehand sketches into photorealistic images remains a fundamental challenge in image synthesis, particularly due to the abstract, sparse, and stylistically diverse nat…
Test-Time Adaptation for Anomaly Segmentation via Topology-Aware Optimal Transport Chaining
Ali Zia, Usman Ali, Umer Ramzan +3
Deep topological data analysis (TDA) offers a principled framework for capturing structural invariants such as connectivity and cycles that persist across scales, making it a natur…
2D_3D Feature Fusion via Cross-Modal Latent Synthesis and Attention Guided Restoration for Industrial Anomaly Detection
Usman Ali, Ali Zia, Abdul Rehman +5
Industrial anomaly detection (IAD) increasingly benefits from integrating 2D and 3D data, but robust cross-modal fusion remains challenging. We propose a novel unsupervised framewo…
Split-Fuse-Transport: Annotation-Free Saliency via Dual Clustering and Optimal Transport Alignment
Muhammad Umer Ramzan, Ali Zia, Abdelwahed Khamis +3
Salient object detection (SOD) aims to segment visually prominent regions in images and serves as a foundational task for various computer vision applications. We posit that SOD ca…
Leveraging Deep Learning with Multi-Head Attention for Accurate Extraction of Medicine from Handwritten Prescriptions
Usman Ali, Sahil Ranmbail, Muhammad Nadeem +3
Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a r…
Gated-Attention Feature-Fusion Based Framework for Poverty Prediction
Muhammad Umer Ramzan, Wahab Khaddim, Muhammad Ehsan Rana +4
This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods li…
Locally-Focused Face Representation for Sketch-to-Image Generation Using Noise-Induced Refinement
Muhammad Umer Ramzan, Ali Zia, Abdelwahed Khamis +3
This paper presents a novel deep-learning framework that significantly enhances the transformation of rudimentary face sketches into high-fidelity colour images. Employing a Convol…
Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security
Muhammad Umer Ramzan, Usman Ali, Syed Haider Abbas Naqvi +4
The auto-management of vehicle entrance and parking in any organization is a complex challenge encompassing record-keeping, efficiency, and security concerns. Manual methods for tr…