New research from FIU shows that some visual-language AI models have become particularly susceptible to image-based hacks.

Abstract: Image super resolution focuses on increasing the spatial resolution of low-quality images and enhancing their visual quality. Since the image degradation process is unknown in real-life ...

Deep learning is a subset of machine learning that uses multi-layer neural networks to find patterns in complex, unstructured data like images, text, and audio. What sets deep learning apart is its ...

Unsupervised learning is a machine learning approach where algorithms analyze and identify patterns in datasets without predefined labels or outcomes. Instead of learning from examples with known ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Medical image repositories have been rapidly growing due to the widespread use of imaging techniques, making manual annotation unfeasible. Efficient image retrieval systems are crucial for diagnosing ...

In recent years, computational pathology has undergone an unprecedented development process due to novel trends in digital imaging technologies and deep learning mechanisms 1. The analysis of ...

Devices really do help make learning more flexible, accessible and engaging. Christina Barreto Sixth grade, Yonkers, N.Y. Teachers are constantly competing with their Chromebooks for attention. Wesley ...

Abstract: Unsupervised domain adaptation (UDA) addresses the domain shift problem by transferring knowledge from labeled source domain data (e.g. CT) to unlabeled target domain data (e.g. MRI). While ...

Crop segmentation, the process of identifying crop regions in images, is fundamental to agricultural monitoring tasks such as yield prediction, pest detection, and growth assessment. Traditional ...