Artificial Intelligence Medical Compendium

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

Showing 46,531 to 46,540 of 224,199 articles

No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency

arXiv Feb 27, 2026
We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X data, where most RGB-X prior work assumes such pairs exist and focuses on... read more 
Computer Vision and Pattern Recognition

CycleBEV: Regularizing View Transformation Networks via View Cycle Consistency for Bird's-Eye-View Semantic Segmentation

arXiv Feb 27, 2026
Transforming image features from perspective view (PV) space to bird's-eye-view (BEV) space remains challenging in autonomous driving due to depth ambiguity and occlusion. Although several view transformation (VT) paradigms have been proposed, the ch... read more 
Computer Vision and Pattern Recognition cs.AI

Hyperdimensional Cross-Modal Alignment of Frozen Language and Image Models for Efficient Image Captioning

arXiv Feb 27, 2026
Large unimodal foundation models for vision and language encode rich semantic structures, yet aligning them typically requires computationally intensive multimodal fine-tuning. Such approaches depend on large-scale parameter updates, are resource int... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG

Pseudo Contrastive Learning for Diagram Comprehension in Multimodal Models

arXiv Feb 27, 2026
Recent multimodal models such as Contrastive Language-Image Pre-training (CLIP) have shown remarkable ability to align visual and linguistic representations. However, domains where small visual differences carry large semantic significance, such as d... read more 
Computer Vision and Pattern Recognition cs.AI

Incremental dimension reduction for efficient and accurate visual anomaly detection

arXiv Feb 27, 2026
While nowadays visual anomaly detection algorithms use deep neural networks to extract salient features from images, the high dimensionality of extracted features makes it difficult to apply those algorithms to large data with 1000s of images. To add... read more 
Computer Vision and Pattern Recognition

When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion

arXiv Feb 27, 2026
Machine learning holds promise for advancing clinical decision support, yet it remains unclear when multimodal learning truly helps in practice, particularly under modality missingness and fairness constraints. In this work, we conduct a systematic b... read more 
Machine Learning cs.AI

Annotation-Free Visual Reasoning for High-Resolution Large Multimodal Models via Reinforcement Learning

arXiv Feb 27, 2026
Current Large Multimodal Models (LMMs) struggle with high-resolution visual inputs during the reasoning process, as the number of image tokens increases quadratically with resolution, introducing substantial redundancy and irrelevant information. A c... read more 
Computer Vision and Pattern Recognition

ReDON: Recurrent Diffractive Optical Neural Processor with Reconfigurable Self-Modulated Nonlinearity

arXiv Feb 27, 2026
Diffractive optical neural networks (DONNs) have demonstrated unparalleled energy efficiency and parallelism by processing information directly in the optical domain. However, their computational expressivity is constrained by static, passive diffrac... read more 
Optics cs.AI cs.ET

DLEBench: Evaluating Small-scale Object Editing Ability for Instruction-based Image Editing Model

arXiv Feb 27, 2026
Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit s... read more 
Computer Vision and Pattern Recognition cs.AI

BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization

arXiv Feb 27, 2026
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of different... read more 
Machine Learning
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