Ophthalmology

Crop-OCT: a Fully Integrated Imageomics Pipeline to Identify Regional and Focal Retinopathy in Murine Models

Imageomics uses machine learning to accelerate our understanding of biological traits and human disease processes. Some of the earliest imageomics applications used deep learning to assess human diseases. For example, retinal fundus images were analyzed to diagnose diabetic retinopathy. The imaging modality optical coherence tomography (OCT) is widely used to diagnose and monitor the progression o...

ATA: Bridging Implicit Reasoning with Attention-Guided and Action-Guided Inference for Vision-Language Action Models

Vision-Language-Action (VLA) models rely on current observations, including images, language instructions, and robot states, to predict actions and complete tasks. While accurate visual perception is crucial for precise action prediction and execution, recent work has attempted to further improve performance by introducing explicit reasoning during inference. However, such approaches face signific...

Rate-Distortion Signatures of Generalization and Information Trade-offs

Generalization to novel visual conditions remains a central challenge for both human and machine vision, yet standard robustness metrics offer limited...

Shape-Interpretable Visual Self-Modeling Enables Geometry-Aware Continuum Robot Control

Continuum robots possess high flexibility and redundancy, making them well suited for safe interaction in complex environments, yet their continuous d...

Learning to Read Where to Look: Disease-Aware Vision-Language Pretraining for 3D CT

Recent 3D CT vision-language models align volumes with reports via contrastive pretraining, but typically rely on limited public data and provide only...

Leveraging Model Soups to Classify Intangible Cultural Heritage Images from the Mekong Delta

The classification of Intangible Cultural Heritage (ICH) images in the Mekong Delta poses unique challenges due to limited annotated data, high visual...

Vision-Language Feature Alignment for Road Anomaly Segmentation

Safe autonomous systems in complex environments require robust road anomaly segmentation to identify unknown obstacles. However, existing approaches o...

Unified Vision-Language Modeling via Concept Space Alignment

We introduce V-SONAR, a vision-language embedding space extended from the text-only embedding space SONAR (Omnilingual Embeddings Team et al., 2026), ...

GroundedSurg: A Multi-Procedure Benchmark for Language-Conditioned Surgical Tool Segmentation

Clinically reliable perception of surgical scenes is essential for advancing intelligent, context-aware intraoperative assistance such as instrument h...

GuiDINO: Rethinking Vision Foundation Model in Medical Image Segmentation

Foundation vision models are increasingly adopted in medical image analysis. Due to domain shift, these pretrained models misalign with medical image ...

ClinCoT: Clinical-Aware Visual Chain-of-Thought for Medical Vision Language Models

Medical Vision-Language Models have shown promising potential in clinical decision support, yet they remain prone to factual hallucinations due to ins...

AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have adopted visual token pruning strategies to mitigate substantial computational overhead incurred by extensive...

When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains

Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medic...

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

Transforming image features from perspective view (PV) space to bird's-eye-view (BEV) space remains challenging in autonomous driving due to depth amb...

Pseudo Contrastive Learning for Diagram Comprehension in Multimodal Models

Recent multimodal models such as Contrastive Language-Image Pre-training (CLIP) have shown remarkable ability to align visual and linguistic represent...

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

Current Large Multimodal Models (LMMs) struggle with high-resolution visual inputs during the reasoning process, as the number of image tokens increas...

3D Modality-Aware Pre-training for Vision-Language Model in MRI Multi-organ Abnormality Detection

Vision-language models (VLMs) show strong potential for complex diagnostic tasks in medical imaging. However, applying VLMs to multi-organ medical ima...

Vision-Language Semantic Grounding for Multi-Domain Crop-Weed Segmentation

Fine-grained crop-weed segmentation is essential for enabling targeted herbicide application in precision agriculture. However, existing deep learning...

StemVLA:An Open-Source Vision-Language-Action Model with Future 3D Spatial Geometry Knowledge and 4D Historical Representation

Vision-language-action (VLA) models integrate visual observations and language instructions to predict robot actions, demonstrating promising generali...

OPTIAGENT: A Physics-Driven Agentic Framework for Automated Optical Design

Optical design is the process of configuring optical elements to precisely manipulate light for high-fidelity imaging. It is inherently a highly non-c...