3 papers
cs.CV2026
Scale-Aware Self-Supervised Learning for Segmentation of Small and Sparse Structures
Jorge Quesada, Ghassan AlRegib
Self-supervised learning (SSL) has emerged as a powerful strategy for representation learning under limited annotation regimes, yet its effectiveness remains highly sensitive to ma…
cs.CV2026
A Large-scale Benchmark on Geological Fault Delineation Models: Domain Shift, Training Dynamics, Generalizability, Evaluation and Inferential Behavior
Jorge Quesada, Chen Zhou, Prithwijit Chowdhury +5
Machine learning has taken a critical role in seismic interpretation workflows, especially in fault delineation tasks. However, despite the recent proliferation of pretrained model…
cs.CV2024
Benchmarking Human and Automated Prompting in the Segment Anything Model
Jorge Quesada, Zoe Fowler, Mohammad Alotaibi +2
The remarkable capabilities of the Segment Anything Model (SAM) for tackling image segmentation tasks in an intuitive and interactive manner has sparked interest in the design of e…