68 citations · 79 across the 7 of their papers we have counts for
7 papers
Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning
Hasindri Watawana, Kanchana Ranasinghe, Tariq Mahmood +3
Self-supervised representation learning has been highly promising for histopathology image analysis with numerous approaches leveraging their patient-slide-patch hierarchy to learn…
Self-regulating Prompts: Foundational Model Adaptation without Forgetting
Muhammad Uzair Khattak, Syed Talal Wasim, Muzammal Naseer +3
Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. Conventionally trained using the task-speci…
Foundational Models Defining a New Era in Vision: A Survey and Outlook
Muhammad Awais, Muzammal Naseer, Salman Khan +5
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world. The complex relations between objects and their locatio…
Boosting Adversarial Transferability using Dynamic Cues
Muzammal Naseer, Ahmad Mahmood, Salman Khan +1
The transferability of adversarial perturbations between image models has been extensively studied. In this case, an attack is generated from a known surrogate \eg, the ImageNet tr…
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
Mamshad Nayeem Rizve, Navid Kardan, Salman Khan +2
Semi-supervised learning (SSL) is one of the dominant approaches to address the annotation bottleneck of supervised learning. Recent SSL methods can effectively leverage a large re…
Self-Distilled Vision Transformer for Domain Generalization
Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan +2
In the recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networ…