7 papers
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…
GeoRoPE: Ground-Aware Rotary Adaptation for Remote Sensing Foundation Models
Yu Luo, Kun Hu, Mengwei He +7
Remote-sensing foundation models (RSFMs) benefit from pretraining on imagery from multiple sensors and ground sampling distances (GSDs), but such exposure alone does not resolve sc…
PhenoYieldNet: Learning Crop-Aware Phenological Responses for Multi-Crop Yield Prediction
Yu Luo, Xiaogang Zhu, Shan Zeng +4
Accurate crop yield prediction is crucial for sustainable agriculture and global food security. While existing methods are predominantly developed for single-crop prediction, they…
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…
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…
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…