Latest AI and machine learning research in geriatrics for healthcare professionals.
The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in neuroimaging, particularly functional near-infrared spectroscopy (fNIRS), have enabled objective assessment of such skills with high accuracy. However, these techniques are hindered by extensive preprocessing requirements to extract neural biomarker...
Current end-to-end (E2E) and plug-and-play (PnP) image reconstruction algorithms approximate the maximum a posteriori (MAP) estimate but cannot offer sampling from the posterior distribution, like diffusion models. By contrast, it is challenging for diffusion models to be trained in an E2E fashion. This paper introduces a Deep End-to-End Posterior ENergy (DEEPEN) framework, which enables MAP est...
This paper presents a novel approach to improving text-guided image editing using diffusion-based models. Text-guided image editing task poses key c...
Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors ...
Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive...
Scene graph (SG) representations can neatly and efficiently describe scene semantics, which has driven sustained intensive research in SG generation...
Ensuring the safety and well-being of elderly and vulnerable populations in assisted living environments is a critical concern. Computer vision pres...
The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. H...
Recent video diffusion models have enhanced video editing, but it remains challenging to handle instructional editing and diverse tasks (e.g., addin...
Accurate transformation estimation between camera space and robot space is essential. Traditional methods using markers for hand-eye calibration req...
Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning method...
Recent advancements in Large Language Models (LLMs) have demonstrated enhanced reasoning capabilities, evolving from Chain-of-Thought (CoT) promptin...
Understanding long video content is a complex endeavor that often relies on densely sampled frame captions or end-to-end feature selectors, yet thes...
Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and ...
Efficiently modeling massive images is a long-standing challenge in machine learning. To this end, we introduce Multi-Scale Attention (MSA). MSA rel...
Recent progress in (multimodal) large language models ((M)LLMs) has shifted focus from pre-training to inference-time compute scaling and post-train...
This paper investigates whether sequence models can learn to perform numerical algorithms, e.g. gradient descent, on the fundamental problem of leas...
Long COVID continues to challenge public health by affecting a considerable number of individuals who have recovered from acute SARS-CoV-2 infection...
Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence. Early detection...
Localization is one of the core parts of modern robotics. Classic localization methods typically follow the retrieve-then-register paradigm, achievi...