58 citations · 73 across the 25 of their papers we have counts for
26 papers
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…
TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection
Farid Hazratian, Ali Zia, Hien Duy Nguyen
Out-of-distribution (OOD) robustness is difficult to diagnose when target-domain labels are unavailable. We consider a more restrictive source-only variant of unsupervised accuracy…
Implicit spatial-frequency fusion of hyperspectral and lidar data via kolmogorov-arnold networks
Zekun Long, Judy X. Yang, Jing Wang +3
Hyperspectral image (HSI) classification is challenging in complex scenes due to spectral ambiguity, spatial heterogeneity, and the strong coupling between material properties and…
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…
WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion
Zekun Long, Ali Zia, Guanyiman Fu +2
Weak spectral responses in hyperspectral images are often obscured by dominant endmembers and sensor noise, resulting in inaccurate abundance estimation. This paper introduces WS-N…
Representation Geometry as a Diagnostic for Out-of-Distribution Robustness
Ali Zia, Farid Hazratian
Robust generalization under distribution shift remains difficult to monitor and optimize in the absence of target-domain labels, as models with similar in-distribution accuracy can…
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…
Hypergraph Contrastive Sensor Fusion for Multimodal Fault Diagnosis in Induction Motors
Usman Ali, Ali Zia, Waqas Ali +4
Reliable induction motor (IM) fault diagnosis is vital for industrial safety and operational continuity, mitigating costly unplanned downtime. Conventional approaches often struggl…
A Review on Coarse to Fine-Grained Animal Action Recognition
Ali Zia, Renuka Sharma, Abdelwahed Khamis +8
This review provides an in-depth exploration of the field of animal action recognition, focusing on coarse-grained (CG) and fine-grained (FG) techniques. The primary aim is to exam…
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
Haofan Wu, Yin Huang, Yuqing Wu +12
High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variabili…
Determining Mosaic Resilience in Sugarcane Plants using Hyperspectral Images
Ali Zia, Jun Zhou, Muyiwa Olayemi
Sugarcane mosaic disease poses a serious threat to the Australian sugarcane industry, leading to yield losses of up to 30% in susceptible varieties. Existing manual inspection meth…
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…
NeFF-BioNet: Crop Biomass Prediction from Point Cloud to Drone Imagery
Xuesong Li, Zeeshan Hayder, Ali Zia +7
Crop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement met…
Collaborative Static-Dynamic Teaching: A Semi-Supervised Framework for Stripe-Like Space Target Detection
Zijian Zhu, Ali Zia, Xuesong Li +6
Stripe-like space target detection (SSTD) is crucial for space situational awareness. Traditional unsupervised methods often fail in low signal-to-noise ratio and variable stripe-l…
SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision
Zijian Zhu, Ali Zia, Xuesong Li +4
Stripe-like space target detection (SSTD) plays a key role in enhancing space situational awareness and assessing spacecraft behaviour. This domain faces three challenges: the lack…
MMCBE: Multi-modality Dataset for Crop Biomass Prediction and Beyond
Xuesong Li, Zeeshan Hayder, Ali Zia +7
Crop biomass, a critical indicator of plant growth, health, and productivity, is invaluable for crop breeding programs and agronomic research. However, the accurate and scalable qu…
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen +40
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: h…
ICML 2023 Topological Deep Learning Challenge : Design and Results
Mathilde Papillon, Mustafa Hajij, Helen Jenne +53
This paper presents the computational challenge on topological deep learning that was hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The competi…
High Spectral Spatial Resolution Synthetic HyperSpectral Dataset form multi-source fusion
Yajie Sun, Ali Zia, Jun Zhou
This research paper introduces a synthetic hyperspectral dataset that combines high spectral and spatial resolution imaging to achieve a comprehensive, accurate, and detailed repre…
CVB: A Video Dataset of Cattle Visual Behaviors
Ali Zia, Renuka Sharma, Reza Arablouei +7
Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the u…