Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

In-context learning, a paradigm bridging the gap between pre-training and fine-tuning, has
demonstrated high efficacy in several NLP tasks, especially in few-shot settings. Despite …

In this paper, we tackle the challenge of white-box false positive adversarial attacks on
contrastive loss based offline handwritten signature verification models. We propose a novel …

Augmented Reality (AR) can enable shared virtual experiences between multiple users. In
order to do so, it is crucial for multi-user AR applications to establish a consensus on the" …

Recent advancements in generative AI, particularly Latent Diffusion Models (LDMs), have
revolutionized image synthesis and manipulation. However, these generative techniques …

Despite being widely applied due to their exceptional capabilities, Large Language Models
(LLMs) have been proven to be vulnerable to backdoor attacks. These attacks introduce …

Neural networks, despite their remarkable performance in widespread applications,
including image classification, are also known to be vulnerable to subtle adversarial noise …

Data augmentation with mixup has been proven effective in various machine learning tasks.
However, previous methods primarily concentrate on generating previously unseen virtual …

Arbitrary Style Transfer (AST) achieves the rendering of real natural images into the painting
styles of arbitrary art style images, promoting art communication. However, misuse of …

Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …