1 citations · 1 across the 4 of their papers we have counts for
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DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe +5
Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework th…
GVD: Guiding Video Diffusion Model for Scalable Video Distillation
Kunyang Li, Jeffrey A Chan Santiago, Sarinda Dhanesh Samarasinghe +2
To address the larger computation and storage requirements associated with large video datasets, video dataset distillation aims to capture spatial and temporal information in a si…
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
Sarinda Samarasinghe, Mamshad Nayeem Rizve, Navid Kardan +1
Few-shot video action recognition is an effective approach to recognizing new categories with only a few labeled examples, thereby reducing the challenges associated with collectin…
Adversarial Training for Face Recognition Systems using Contrastive Adversarial Learning and Triplet Loss Fine-tuning
Nazmul Karim, Umar Khalid, Nick Meeker +1
Though much work has been done in the domain of improving the adversarial robustness of facial recognition systems, a surprisingly small percentage of it has focused on self-superv…