5 citations · 9 across the 5 of their papers we have counts for
7 papers
Cross-Dataset Transfer and Unknown-Class Detection in Imbalanced SAR Ship Classification
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni +1
Ship classification from Synthetic Aperture Radar (SAR) imagery is a critical computer vision task, yet the robustness of models under deployment shifts remains unclear. While mode…
Enhancing Foundation Models for Imbalanced SAR Ship Classification via Targeted Oversampling
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni
Remote-sensing foundation models offer strong representations for SAR imagery, but their behavior under severe long-tail class imbalance is still not well characterized. We benchma…
Feature-Space Oversampling for Addressing Class Imbalance in SAR Ship Classification
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni +1
SAR ship classification faces the challenge of long-tailed datasets, which complicates the classification of underrepresented classes. Oversampling methods have proven effective in…
A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni +1
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, includi…
A Survey on SAR ship classification using Deep Learning
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni +1
Deep learning (DL) has emerged as a powerful tool for Synthetic Aperture Radar (SAR) ship classification. This survey comprehensively analyzes the diverse DL techniques employed in…
A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction
Ch Muhammad Awais, Wei Gu, Gcinizwe Dlamini +2
Is there a statistical difference between Naive Bayes and Random Forest in terms of recall, f-measure, and precision for predicting software defects? By utilizing systematic litera…
What augmentations are sensitive to hyper-parameters and why?
Ch Muhammad Awais, Imad Eddine Ibrahim Bekkouch
We apply augmentations to our dataset to enhance the quality of our predictions and make our final models more resilient to noisy data and domain drifts. Yet the question remains,…