Ophthalmology

Vision-Language Attribute Disentanglement and Reinforcement for Lifelong Person Re-Identification

Lifelong person re-identification (LReID) aims to learn from varying domains to obtain a unified person retrieval model. Existing LReID approaches typically focus on learning from scratch or a visual classification-pretrained model, while the Vision-Language Model (VLM) has shown generalizable knowledge in a variety of tasks. Although existing methods can be directly adapted to the VLM, since they...

Evaluating Vision Foundation Models for Pixel and Object Classification in Microscopy

Deep learning underlies most modern approaches and tools in computer vision, including biomedical imaging. However, for interactive semantic segmentation (often called pixel classification in this context) and interactive object-level classification (object classification), feature-based shallow learning remains widely used. This is due to the diversity of data in this domain, the lack of large pr...

Real-Time Structural Detection for Indoor Navigation from 3D LiDAR Using Bird's-Eye-View Images

Efficient structural perception is essential for mapping and autonomous navigation on resource-constrained robots. Existing 3D methods are computation...

MFil-Mamba: Multi-Filter Scanning for Spatial Redundancy-Aware Visual State Space Models

State Space Models (SSMs), especially recent Mamba architecture, have achieved remarkable success in sequence modeling tasks. However, extending SSMs ...

Tinted Frames: Question Framing Blinds Vision-Language Models

Vision-Language Models (VLMs) have been shown to be blind, often underutilizing their visual inputs even on tasks that require visual reasoning. In th...

Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following

Large vision language models (LVLMs) have demonstrated impressive performance across a wide range of tasks. These capabilities largely stem from visua...

Vision Tiny Recursion Model (ViTRM): Parameter-Efficient Image Classification via Recursive State Refinement

The success of deep learning in computer vision has been driven by models of increasing scale, from deep Convolutional Neural Networks (CNN) to large ...

Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis

Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their applicatio...

A Cross-Study Multi-Organ Cell Atlas ofMacaca fascicularis Informed by Human Foundation Model Annotation: A Resource for Translational Target Assessment

Non-human primates (NHPs), particularly Macaca fascicularis (cynomolgus macaque), represent an essential model for preclinical assessment of biologics...

Counting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language Models

Counting serves as a simple but powerful test of a Large Vision-Language Model's (LVLM's) reasoning; it forces the model to identify each individual o...

CoDA: Exploring Chain-of-Distribution Attacks and Post-Hoc Token-Space Repair for Medical Vision-Language Models

Medical vision--language models (MVLMs) are increasingly used as perceptual backbones in radiology pipelines and as the visual front end of multimodal...

SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation

Realistic and efficient 3D garment generation remains a longstanding challenge in computer vision and digital fashion. Existing methods typically rely...

Tinted Frames: Question Framing Blinds Vision-Language Models

Vision-Language Models (VLMs) have been shown to be blind, often underutilizing their visual inputs even on tasks that require visual reasoning. In th...

Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders

Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightwe...

DriveTok: 3D Driving Scene Tokenization for Unified Multi-View Reconstruction and Understanding

With the growing adoption of vision-language-action models and world models in autonomous driving systems, scalable image tokenization becomes crucial...

LLM-Augmented Computational Phenotyping of Long Covid

Phenotypic characterization is essential for understanding heterogeneity in chronic diseases and for guiding personalized interventions. Long COVID, a...

CycleCap: Improving VLMs Captioning Performance via Self-Supervised Cycle Consistency Fine-Tuning

Visual-Language Models (VLMs) have achieved remarkable progress in image captioning, visual question answering, and visual reasoning. Yet they remain ...

From Drop-off to Recovery: A Mechanistic Analysis of Segmentation in MLLMs

Multimodal Large Language Models (MLLMs) are increasingly applied to pixel-level vision tasks, yet their intrinsic capacity for spatial understanding ...

MedSAD-CLIP: Supervised CLIP with Token-Patch Cross-Attention for Medical Anomaly Detection and Segmentation

Medical anomaly detection (MAD) and segmentation play a critical role in assisting clinical diagnosis by identifying abnormal regions in medical image...

FineViT: Progressively Unlocking Fine-Grained Perception with Dense Recaptions

While Multimodal Large Language Models (MLLMs) have experienced rapid advancements, their visual encoders frequently remain a performance bottleneck. ...