Latest AI and machine learning research in medicare for healthcare professionals.
Deformable image registration is a fundamental requirement for medical image analysis. Recently, transformers have been widely used in deep learning-based registration methods for their ability to capture long-range dependency via self-attention (SA). However, the high computation and memory loads of SA (growing quadratically with the spatial resolution) hinder transformers from processing subtl...
Recent advances in flexible keyword spotting (KWS) with text enrollment allow users to personalize keywords without uttering them during enrollment. However, there is still room for improvement in target keyword performance. In this work, we propose a novel few-shot transfer learning method, called text-aware adapter (TA-adapter), designed to enhance a pre-trained flexible KWS model for specific...
Row-level lineage explains what input rows produce an output row through a data processing pipeline, having many applications like data debugging, a...
Seagrass meadows play a crucial role in marine ecosystems, providing benefits such as carbon sequestration, water quality improvement, and habitat p...
In this work, we introduce the task of life-long personalization of large language models. While recent mainstream efforts in the LLM community main...
Visual Language Models (VLMs) demonstrate impressive capabilities in processing multimodal inputs, yet applications such as visual agents, which req...
Multispectral point cloud (MPC) captures 3D spatial-spectral information from the observed scene, which can be used for scene understanding and has ...
The rapid advancement of deep learning has intensified the need for comprehensive data for use by autonomous driving algorithms. High-quality datase...
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...
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...
Coverage path planning (CPP) is the task of computing an optimal path within a region to completely scan or survey an area of interest using one or ...
Single image super-resolution (SR) has long posed a challenge in the field of computer vision. While the advent of deep learning has led to the emer...
Long-read sequencing technologies can capture entire RNA transcripts in a single sequencing read, reducing the ambiguity in constructing and quantifyi...
Emergency response times are critical in densely populated urban environments like New York City (NYC), where traffic congestion significantly imped...
Maritime environmental sensing requires overcoming challenges from complex conditions such as harsh weather, platform perturbations, large dynamic o...
Accurate prediction of medical conditions with straight past clinical evidence is a long-sought topic in the medical management and health insurance...
Until now, it has been difficult for volumetric super-resolution to utilize the recent advances in transformer-based models seen in 2D super-resolut...
Measuring Internet outages is important to allow ISPs to improve their services, users to choose providers by reliability, and governments to unders...
Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction sy...