Prompt engineering is a technique that involves augmenting a large pre-trained model with
task-specific hints, known as prompts, to adapt the model to new tasks. Prompts can be …

Recently, Machine Unlearning (MU) has gained considerable attention for its potential to
improve AI safety by removing the influence of specific data from trained Machine Learning …

While existing backdoor attacks have successfully infected multimodal contrastive learning
models such as CLIP they can be easily countered by specialized backdoor defenses for …

Abstract Contrastive Vision-Language Pre-training known as CLIP has shown promising
effectiveness in addressing downstream image recognition tasks. However recent works …

Machine unlearning (MU) is gaining increasing attention due to the need to remove or
modify predictions made by machine learning (ML) models. While training models have …

Machine Unlearning, a pivotal field addressing data privacy in machine learning,
necessitates efficient methods for the removal of private or irrelevant data. In this context …

Adversarial phenomenon has been widely observed in machine learning (ML) systems,
especially in those using deep neural networks, describing that ML systems may produce …

Contrastive vision-language representation learning has achieved state-of-the-art
performance for zero-shot classification, by learning from millions of image-caption pairs …

Autoregressive Visual Language Models (VLMs) showcase impressive few-shot learning
capabilities in a multimodal context. Recently, multimodal instruction tuning has been …

Recent advancements in deep learning have shown that multimodal inference can be
particularly useful in tasks like autonomous driving, human health, and production line …