Dr. Eman M El-Sheikh

Integrating new class information without losing previously acquired knowledge remains a central challenge in artificial intelligence, often referred to as catastrophic forgetting. Few-shot class incremental learning (FSCIL) addresses this by first training a model on a robust dataset of base classes and then incrementally adapting it in successive sessions using only a few labeled examples per novel class. However, this approach is prone to overfitting on the limited new data, which can compromise overall performance and exacerbate forgetting. In this work, we propose a simple yet effective novel FSCIL framework that leverages a frozen Vision Transformer (ViT) backbone augmented with parameter-efficient additive updates. Our approach freezes the
pre-trained ViT parameters and selectively injects trainable weights into the self-attention modules via an additive update mechanism. This design updates only a small subset of parameters to accommodate new classes without sacrificing the representations learned during the base session. By fine-tuning a limited number of parameters, our method preserves the generalizable features in the frozen ViT while reducing the risk of overfitting. Furthermore, as most parameters remain fixed, the model avoids overwriting previously learned knowledge when small novel data batches are introduced. Extensive experiments on benchmark datasets demonstrate that our approach yields state-of-the-art performance compared to baseline FSCIL methods.

Backdoor attacks pose a critical threat by embedding hidden triggers into inputs, causing models to misclassify them into target labels. While extensive
research has focused on mitigating these attacks in object recognition models through weight fine-tuning, much less attention has been given to detecting
backdoored samples directly. Given the vast datasets used in training, manual inspection for backdoor triggers is impractical, and even state-of-the-art
defense mechanisms fail to fully neutralize their impact. To address this gap, we introduce a groundbreaking method to detect unseen backdoored images during both training and inference. Leveraging the transformative success of prompt tuning in Vision Language Models (VLMs), our approach trains learnable text prompts to differentiate clean images from those with hidden backdoor triggers. Experiments demonstrate the exceptional efficacy of this method, achieving an impressive average accuracy of 86% across two renowned datasets for detecting unseen backdoor triggers, establishing a new standard in backdoor defense.