Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 45,181 to 45,190 of 224,055 articles

From Local Matches to Global Masks: Novel Instance Detection in Open-World Scenes

arXiv Mar 3, 2026
Detecting and segmenting novel object instances in open-world environments is a fundamental problem in robotic perception. Given only a small set of template images, a robot must locate and segment a specific object instance in a cluttered, previousl... read more 
Computer Vision and Pattern Recognition cs.RO

An Effective Data Augmentation Method by Asking Questions about Scene Text Images

arXiv Mar 3, 2026
Scene text recognition (STR) and handwritten text recognition (HTR) face significant challenges in accurately transcribing textual content from images into machine-readable formats. Conventional OCR models often predict transcriptions directly, which... read more 
Computer Vision and Pattern Recognition

MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

arXiv Mar 3, 2026
Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs. We present MUSE (Mul... read more 
Machine Learning cs.CL cs.CV cs.SD eess.AS

The Perceptual Gap: Why We Need Accessible XAI for Assistive Technologies

arXiv Mar 3, 2026
Artificial intelligence systems are widely used by people with sensory disabilities, like loss of vision or hearing, to help perceive or navigate the world around them. This includes tasks like describing an image or object they cannot touch, reading... read more 
Human-Computer Interaction

Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters

arXiv Mar 3, 2026
This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the proposed method extracts biomechanically meaningful markers analogous to... read more 
Image and Video Processing cs.CV

NeighborMAE: Exploiting Spatial Dependencies between Neighboring Earth Observation Images in Masked Autoencoders Pretraining

arXiv Mar 3, 2026
Masked Image Modeling has been one of the most popular self-supervised learning paradigms to learn representations from large-scale, unlabeled Earth Observation images. While incorporating multi-modal and multi-temporal Earth Observation data into Ma... read more 
Computer Vision and Pattern Recognition

LLM-MLFFN: Multi-Level Autonomous Driving Behavior Feature Fusion via Large Language Model

arXiv Mar 3, 2026
Accurate classification of autonomous vehicle (AV) driving behaviors is critical for safety validation, performance diagnosis, and traffic integration analysis. However, existing approaches primarily rely on numerical time-series modeling and often l... read more 
Artificial Intelligence cs.RO

ForestPersons: A Large-Scale Dataset for Under-Canopy Missing Person Detection

arXiv Mar 3, 2026
Detecting missing persons in forest environments remains a challenge, as dense canopy cover often conceals individuals from detection in top-down or oblique aerial imagery typically captured by Unmanned Aerial Vehicles (UAVs). While UAVs are effectiv... read more 
Computer Vision and Pattern Recognition

SemGS: Feed-Forward Semantic 3D Gaussian Splatting from Sparse Views for Generalizable Scene Understanding

arXiv Mar 3, 2026
Semantic understanding of 3D scenes is essential for robots to operate effectively and safely in complex environments. Existing methods for semantic scene reconstruction and semantic-aware novel view synthesis often rely on dense multi-view inputs an... read more 
Computer Vision and Pattern Recognition

PatientFlow: Learning to generate mixed-type longitudinal clinical data with flow matching.

Artificial intelligence in medicine Mar 3, 2026
Synthetic longitudinal clinical data, with static and temporal mixed-type components, can help unlock large-scale deep learning models to tackle complex diseases. However, learning to generate realistic patients faces dual challenges: modeling the in... read more 
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