Latest AI and machine learning research in geriatrics for healthcare professionals.
Crop row detection is essential for enabling autonomous navigation in GPS-denied environments, such as under-canopy agricultural settings. Traditional methods often struggle with occlusions, variable lighting conditions, and the structural variability of crop rows. To address these challenges, RowDetr, a novel end-to-end neural network architecture, is introduced for robust and efficient row det...
Vision-based autonomous driving shows great potential due to its satisfactory performance and low costs. Most existing methods adopt dense representations (e.g., bird's eye view) or sparse representations (e.g., instance boxes) for decision-making, which suffer from the trade-off between comprehensiveness and efficiency. This paper explores a Gaussian-centric end-to-end autonomous driving (Gauss...
DNN-based watermarking methods have rapidly advanced, with the ``Encoder-Noise Layer-Decoder'' (END) framework being the most widely used. To ensure...
This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and intera...
End-to-end optimization, which simultaneously optimizes optics and algorithms, has emerged as a powerful data-driven method for computational imagin...
As society rapidly digitizes, successful aging necessitates using technology for health and social care and social engagement. Technologies aimed to s...
The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dement...
End-to-end autonomous driving has received increasing attention due to its potential to learn from large amounts of data. However, most existing met...
Vision-Language Models (VLMs) have shown promising capabilities in handling various multimodal tasks, yet they struggle in long-context scenarios, p...
We introduce Neptune, a benchmark for long video understanding that requires reasoning over long time horizons and across different modalities. Many...
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (...
Knee osteoporosis weakens the bone tissue in the knee joint, increasing fracture risk. Early detection through X-ray images enables timely intervent...
Image retrieval methods rely on metric learning to train backbone feature extraction models that can extract discriminant queries and reference (gal...
We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose ma...
Recently, we have witnessed the rise of novel ``event-based'' camera sensors for high-speed, low-power video capture. Rather than recording discrete...
Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare persp...
Large-scale 3D point clouds (LS3DPC) obtained by LiDAR scanners require huge storage space and transmission bandwidth due to a large amount of data....
Arbitrary Style Transfer (AST) achieves the rendering of real natural images into the painting styles of arbitrary art style images, promoting art c...
Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition err...
Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer's disease (AD) detection methods are characterized by...