82 citations · 153 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal
Wanli Ma, Oktay Karakus, Paul L. Rosin
Cloud removal plays a crucial role in enhancing remote sensing image analysis, yet accurately reconstructing cloud-obscured regions remains a significant challenge. Recent advancem…
Integrating Semi-Supervised and Active Learning for Semantic Segmentation
Wanli Ma, Oktay Karakus, Paul L. Rosin
In this paper, we propose a novel active learning approach integrated with an improved semi-supervised learning framework to reduce the cost of manual annotation and enhance model…
Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning
Wanli Ma, Oktay Karakus, Paul L. Rosin
The advancement of knowledge distillation has played a crucial role in enabling the transfer of knowledge from larger teacher models to smaller and more efficient student models, a…
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery
Wanli Ma, Oktay Karakus, Paul L. Rosin
Semi-supervised learning (SSL) aims to help reduce the cost of the manual labelling process by leveraging a substantial pool of unlabelled data alongside a limited set of labelled…
Confidence-Guided Semi-supervised Learning in Land Cover Classification
Wanli Ma, Oktay Karakus, Paul L. Rosin
Semi-supervised learning has been well developed to help reduce the cost of manual labelling by exploiting a large quantity of unlabelled data. Especially in the application of lan…