Artificial Intelligence Medical Compendium

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

Showing 27,761 to 27,770 of 219,020 articles

Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning

arXiv Apr 23, 2026
Self-supervised learning (SSL) is a standard approach for representation learning in aerial imagery. Existing methods enforce invariance between augmented views, which works well when augmentations preserve semantic content. However, aerial images ar... read more 
Computer Vision and Pattern Recognition cs.AI cs.LG cs.NE

Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning

arXiv Apr 23, 2026
Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bottleneck lies in reasoning or representation. We study this on Bongard-LOGO, a synthetic benchmark ... read more 
Artificial Intelligence cs.CL cs.CV

Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts

arXiv Apr 23, 2026
Charts are widely used to present complex information. Deriving meaningful insights in real-world contexts often requires interpreting multiple related charts together. Research on understanding multi-chart images has not been extensively explored. W... read more 
Computation and Language cs.AI cs.CV cs.LG cs.MA

Latent Denoising Improves Visual Alignment in Large Multimodal Models

arXiv Apr 23, 2026
Large Multimodal Models (LMMs) such as LLaVA are typically trained with an autoregressive language modeling objective, providing only indirect supervision to visual tokens. This often yields weak internal visual representations and brittle behavior u... read more 
Computer Vision and Pattern Recognition

MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment

arXiv Apr 23, 2026
Universal Multimodal Retrieval (UMR) aims to map different modalities (e.g., visual and textual) into a shared embedding space for multi-modal retrieval. Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such a... read more 
Computer Vision and Pattern Recognition cs.AI

Temporal Prototyping and Hierarchical Alignment for Unsupervised Video-based Visible-Infrared Person Re-Identification

arXiv Apr 23, 2026
Visible-infrared person re-identification (VI-ReID) enables cross-modality identity matching for all-day surveillance, yet existing methods predominantly focus on the image level or rely heavily on costly identity annotations. While video-based VI-Re... read more 
Computer Vision and Pattern Recognition

FryNet: Dual-Stream Adversarial Fusion for Non-Destructive Frying Oil Oxidation Assessment

arXiv Apr 23, 2026
Monitoring frying oil degradation is critical for food safety, yet current practice relies on destructive wet-chemistry assays that provide no spatial information and are unsuitable for real-time use. We identify a fundamental obstacle in thermal-ima... read more 
Computer Vision and Pattern Recognition

The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

arXiv Apr 23, 2026
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs gener... read more 
Computer Vision and Pattern Recognition cs.AI

an interpretable vision transformer framework for automated brain tumor classification

arXiv Apr 23, 2026
Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient survival rates. Manual interpretation of Magnetic Resonance Imaging (MRI) scans is time-intensive, subject... read more 
Computer Vision and Pattern Recognition

GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA

arXiv Apr 23, 2026
Vision Graph Neural Networks (ViGs) represent an image as a graph of patch tokens, enabling adaptive, feature-driven neighborhoods. Unlike CNNs with fixed grid biases or Vision Transformers with global token interactions, ViGs rely on dynamic graph c... read more 
Computer Vision and Pattern Recognition cs.DC
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