Artificial Intelligence Medical Compendium

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

Showing 53,671 to 53,680 of 225,930 articles

Characterizing Human Semantic Navigation in Concept Production as Trajectories in Embedding Space

arXiv Feb 5, 2026
Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To investigate how humans traverse this geometry, we introduce a framework that represents concept produ... read more 
Computation and Language cs.LG q-bio.NC

RISE-Video: Can Video Generators Decode Implicit World Rules?

arXiv Feb 5, 2026
While generative video models have achieved remarkable visual fidelity, their capacity to internalize and reason over implicit world rules remains a critical yet under-explored frontier. To bridge this gap, we present RISE-Video, a pioneering reasoni... read more 
Computer Vision and Pattern Recognition cs.AI

MambaVF: State Space Model for Efficient Video Fusion

arXiv Feb 5, 2026
Video fusion is a fundamental technique in various video processing tasks. However, existing video fusion methods heavily rely on optical flow estimation and feature warping, resulting in severe computational overhead and limited scalability. This pa... read more 
Computer Vision and Pattern Recognition

Predicting Camera Pose from Perspective Descriptions for Spatial Reasoning

arXiv Feb 5, 2026
Multi-image spatial reasoning remains challenging for current multimodal large language models (MLLMs). While single-view perception is inherently 2D, reasoning over multiple views requires building a coherent scene understanding across viewpoints. I... read more 
Computer Vision and Pattern Recognition

Predicting Camera Pose from Perspective Descriptions for Spatial Reasoning

arXiv Feb 5, 2026
Multi-image spatial reasoning remains challenging for current multimodal large language models (MLLMs). While single-view perception is inherently 2D, reasoning over multiple views requires building a coherent scene understanding across viewpoints. I... read more 
Computer Vision and Pattern Recognition

Shared LoRA Subspaces for almost Strict Continual Learning

arXiv Feb 5, 2026
Adapting large pretrained models to new tasks efficiently and continually is crucial for real-world deployment but remains challenging due to catastrophic forgetting and the high cost of retraining. While parameter-efficient tuning methods like low r... read more 
Machine Learning cs.AI cs.CV

Private and interpretable clinical prediction with quantum-inspired tensor train models

arXiv Feb 5, 2026
Machine learning in clinical settings must balance predictive accuracy, interpretability, and privacy. Models such as logistic regression (LR) offer transparency, while neural networks (NNs) provide greater predictive power; yet both remain vulnerabl... read more 
Machine Learning cs.CR quant-ph

From Blurry to Believable: Enhancing Low-quality Talking Heads with 3D Generative Priors

arXiv Feb 5, 2026
Creating high-fidelity, animatable 3D talking heads is crucial for immersive applications, yet often hindered by the prevalence of low-quality image or video sources, which yield poor 3D reconstructions. In this paper, we introduce SuperHead, a novel... read more 
Computer Vision and Pattern Recognition

Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving

arXiv Feb 5, 2026
Driven by the emergence of Controllable Video Diffusion, existing Sim2Real methods for autonomous driving video generation typically rely on explicit intermediate representations to bridge the domain gap. However, these modalities face a fundamental ... read more 
Computer Vision and Pattern Recognition

MetaSSP: Enhancing Semi-supervised Implicit 3D Reconstruction through Meta-adaptive EMA and SDF-aware Pseudo-label Evaluation

arXiv Feb 5, 2026
Implicit SDF-based methods for single-view 3D reconstruction achieve high-quality surfaces but require large labeled datasets, limiting their scalability. We propose MetaSSP, a novel semi-supervised framework that exploits abundant unlabeled images. ... read more 
Computer Vision and Pattern Recognition
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