Latest AI and machine learning research in adhd/add for healthcare professionals.
Hyperspectral image (HSI) unmixing is a challenging research problem that tries to identify the constituent components, known as endmembers, and their corresponding proportions, known as abundances, in the scene by analysing images captured by hyperspectral cameras. Recently, many deep learning based unmixing approaches have been proposed with the surge of machine learning techniques, especially...
Video editing increasingly demands the ability to incorporate specific real-world instances into existing footage, yet current approaches fundamentally fail to capture the unique visual characteristics of particular subjects and ensure natural instance/scene interactions. We formalize this overlooked yet critical editing paradigm as "Get-In-Video Editing", where users provide reference images to...
Techniques that rigorously bound the overall rounding error exhibited by a numerical program are of significant interest for communities developing ...
String matching is a fundamental problem in computer science, with critical applications in text retrieval, bioinformatics, and data analysis. Among...
One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoi...
In tomato greenhouse, phenotypic measurement is meaningful for researchers and farmers to monitor crop growth, thereby precisely control environment...
Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the per...
Recent years have witnessed a growing academic and industrial interest in deep learning (DL) for medical imaging. To perform well, DL models require...
Whether and how to regulate AI is one of the defining questions of our times - a question that is being debated locally, nationally, and internation...
Text-to-image diffusion models offer powerful image editing capabilities. To edit real images, many methods rely on the inversion of the image into ...
To evaluate end-to-end autonomous driving systems, a simulation environment based on Novel View Synthesis (NVS) techniques is essential, which synth...
According to the World Health Organization, over 466 million people worldwide suffer from disabling hearing loss, with approximately 34 million of t...
Large Language Models (LLMs) have made significant strides in natural language generation but often face challenges in tasks requiring precise calcu...
DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned represe...
Visual Question Answering (VQA) is a challenging problem that requires to process multimodal input. Answer-Set Programming (ASP) has shown great pot...
High-voltage transmission lines are located far from the road, resulting in inconvenient inspection work and rising maintenance costs. Intelligent i...
In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known...
With billions of smartphones in use globally, the daily time spent on these devices contributes significantly to overall electricity consumption. Gi...
Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especi...
We present an approach to model-based RL that achieves a new state of the art performance on the challenging Craftax-classic benchmark, an open-worl...