7 citations · 8 across the 8 of their papers we have counts for
8 papers
Meta-Learning for Data-Efficient Plant Growth Estimation via Vision Transformers and Fuzzy Clustering
Sheikh Hasan Elahi, Rusith Chamara Hathurusinghe Dewage, Habib Ullah +3
Accurate plant growth estimation is essential for greenhouse monitoring, yet obtaining labeled data remains costly and time-consuming. To address this, we propose a few-shot regres…
Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation
Chandler Timm C. Doloriel, Yunbei Zhang, Sarthak Kumar Maharana +5
Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment is fragile under continual test-…
Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting
Chandler Timm C. Doloriel, Yunbei Zhang, Muhammad Salman Siddiqui +4
Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA with batch size one and no acce…
CAST: Closed-form Analytic Semantic Transfer for Zero-Shot Classifier Extension
William Heyden, Habib Ullah, Muhammad Salman Siddiqui +1
Large pre-trained models have become foundational components of modern machine learning systems. Yet adapting these models to novel categories typically requires examples from the…
Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking
Chandler Timm C. Doloriel, Habib Ullah, Kristian Hovde Liland +2
Universal deepfake detection aims to identify AI-generated images across a broad range of generative models, including unseen ones. This requires robust generalization to new and u…
Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)
Fadi Al Machot, Martin Thomas Horsch, Habib Ullah
Growing concerns over the lack of transparency in AI, particularly in high-stakes fields like healthcare and finance, drive the need for explainable and trustworthy systems. While…
Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach
Fadi Al Machot, Martin Thomas Horsch, Habib Ullah
This paper presents a hybrid methodology that enhances the training process of deep learning (DL) models by embedding domain expert knowledge using ontologies and answer set progra…
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning
William Heyden, Habib Ullah, M. Salman Siddiqui +1
In Generalized Zero-Shot Learning (GZSL), we aim to recognize both seen and unseen categories using a model trained only on seen categories. In computer vision, this translates int…