Artificial Intelligence Medical Compendium

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

Showing 34,111 to 34,120 of 221,510 articles

Beyond Semantics: Disentangling Information Scope in Sparse Autoencoders for CLIP

arXiv Apr 7, 2026
Sparse Autoencoders (SAEs) have emerged as a powerful tool for interpreting the internal representations of CLIP vision encoders, yet existing analyses largely focus on the semantic meaning of individual features. We introduce information scope as a ... read more 
Computer Vision and Pattern Recognition

Single-Stage Signal Attenuation Diffusion Model for Low-Light Image Enhancement and Denoising

arXiv Apr 7, 2026
Diffusion models excel at image restoration via probabilistic modeling of forward noise addition and reverse denoising, and their ability to handle complex noise while preserving fine details makes them well-suited for Low-Light Image Enhancement (LL... read more 
Computer Vision and Pattern Recognition

Controllable Image Generation with Composed Parallel Token Prediction

arXiv Apr 7, 2026
Conditional discrete generative models struggle to faithfully compose multiple input conditions. To address this, we derive a theoretically-grounded formulation for composing discrete probabilistic generative processes, with masked generation (absorb... read more 
Machine Learning

ASSR-Net: Anisotropic Structure-Aware and Spectrally Recalibrated Network for Hyperspectral Image Fusion

arXiv Apr 7, 2026
Hyperspectral image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSI) by integrating complementary information from multi-source inputs. Despite recent progress, existing methods still face two critical challenges: (1) ... read more 
Computer Vision and Pattern Recognition

On the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors

arXiv Apr 7, 2026
Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-based compressors built on the Reverse Channel Coding... read more 
Computer Vision and Pattern Recognition cs.AI

SVC 2026: the Second Multimodal Deception Detection Challenge and the First Domain Generalized Remote Physiological Measurement Challenge

arXiv Apr 7, 2026
Subtle visual signals, although difficult to perceive with the naked eye, contain important information that can reveal hidden patterns in visual data. These signals play a key role in many applications, including biometric security, multimedia foren... read more 
Computer Vision and Pattern Recognition

Improving Controllable Generation: Faster Training and Better Performance via $x_0$-Supervision

arXiv Apr 7, 2026
Text-to-Image (T2I) diffusion/flow models have recently achieved remarkable progress in visual fidelity and text alignment. However, they remain limited when users need to precisely control image layouts, something that natural language alone cannot ... read more 
Computer Vision and Pattern Recognition

Sparsity-Aware Voxel Attention and Foreground Modulation for 3D Semantic Scene Completion

arXiv Apr 7, 2026
Monocular Semantic Scene Completion (SSC) aims to reconstruct complete 3D semantic scenes from a single RGB image, offering a cost-effective solution for autonomous driving and robotics. However, the inherently imbalanced nature of voxel distribution... read more 
Computer Vision and Pattern Recognition

RHVI-FDD: A Hierarchical Decoupling Framework for Low-Light Image Enhancement

arXiv Apr 7, 2026
Low-light images often suffer from severe noise, detail loss, and color distortion, which hinder downstream multimedia analysis and retrieval tasks. The degradation in low-light images is complex: luminance and chrominance are coupled, while within t... read more 
Computer Vision and Pattern Recognition

Stealthy and Adjustable Text-Guided Backdoor Attacks on Multimodal Pretrained Models

arXiv Apr 7, 2026
Multimodal pretrained models are vulnerable to backdoor attacks, yet most existing methods rely on visual or multimodal triggers, which are impractical since visually embedded triggers rarely occur in real-world data. To overcome this limitation, we ... read more 
Cryptography and Security cs.LG
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