Deep Neural Networks (DNNs) excel in enhancing surgical precision through semantic segmentation and accurately identifying robotic instruments and tissues. However, they face catastrophic forgetting and a rapid decline in performance on previous tasks when learning...

Advancements in machine learning, specifically in designing neural networks, have made significant strides thanks to Neural Architecture Search (NAS). This technique, which automates the architectural design process, marks a pivotal shift from manual interventions,...

In a recent study, a team of researchers addressed the intrinsic drawbacks of current online content portals that enable users to ask questions to improve their comprehension, especially in learning environments such as lectures. Conventional Information Retrieval...

Diffusion probabilistic models (DPMs) have long been a cornerstone of AI image generation, but their computational demands have been a significant drawback. This paper introduces a novel technique, T-Stitch, which offers a clever solution to this problem. By enhancing...

The recent advancements in machine learning, particularly in generative models, have been marked by the emergence of diffusion models (DMs) as powerful tools for modeling complex data distributions and generating realistic samples across various domains such as...

With the integration of Large Language Models (LLMs) with pre-trained visual encoders, Multimodal Large Language Models (MLLMs) have revolutionized the realm of artificial intelligence. Still, there are challenges, especially in accurately recognizing and...