7 citations · 22 across the 46 of their papers we have counts for
4 papers · 2 filters
SineProject: Machine Unlearning for Stable Vision Language Alignment
Arpit Garg, Hemanth Saratchandran, Simon Lucey
Multimodal Large Language Models (MLLMs) increasingly need to forget specific knowledge such as unsafe or private information without requiring full retraining. However, existing u…
Can You Learn to See Without Images? Procedural Warm-Up for Vision Transformers
Zachary Shinnick, Liangze Jiang, Hemanth Saratchandran +2
Transformers are remarkably versatile, suggesting the existence of generic inductive biases beneficial across modalities. In this work, we explore a new way to instil such biases i…
Structured Initialization for Vision Transformers
Jianqiao Zheng, Xueqian Li, Hemanth Saratchandran +1
Convolutional Neural Networks (CNNs) inherently encode strong inductive biases, enabling effective generalization on small-scale datasets. In this paper, we propose integrating thi…
Enhancing Transformers Through Conditioned Embedded Tokens
Hemanth Saratchandran, Simon Lucey
Transformers have transformed modern machine learning, driving breakthroughs in computer vision, natural language processing, and robotics. At the core of their success lies the at…