Latest AI and machine learning research in ophthalmology for healthcare professionals.
Visual reprogramming (VR) reuses pre-trained vision models for downstream image classification tasks by adding trainable noise patterns to inputs. When applied to vision-language models (e.g., CLIP), existing VR approaches follow the same pipeline used in vision models (e.g., ResNet, ViT), where ground-truth class labels are inserted into fixed text templates to guide the optimization of VR patt...
Compositional Zero-Shot Learning (CZSL) aims to recognize novel attribute-object compositions by leveraging knowledge from seen compositions. Existing methods align textual prototypes with visual features through Vision-Language Models (VLMs), but they face two key limitations: (1) modality gaps hinder the discrimination of semantically similar composition pairs, and (2) single-modal textual pro...
Medical image analysis suffers from a lack of labeled data due to several challenges including patient privacy and lack of experts. Although some AI...
A fundamental challenge in artificial intelligence involves understanding the cognitive mechanisms underlying visual reasoning in sophisticated mode...
Worldwide, sight loss is commonly occurred by retinal diseases, with age-related macular degeneration (AMD) being a notable facet that affects elder...
Image restoration aims to recover content from inputs degraded by various factors, such as adverse weather, blur, and noise. Perceptual Image Restor...
Image loading represents a critical bottleneck in modern machine learning pipelines, particularly in computer vision tasks where JPEG remains the do...
In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of ...
Human Action Recognition (HAR) is a challenging domain in computer vision, involving recognizing complex patterns by analyzing the spatiotemporal dy...
Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is...
Animal stereotypes are deeply embedded in human culture and language. They often shape our perceptions and expectations of various species. Our stud...
We present VARGPT, a novel multimodal large language model (MLLM) that unifies visual understanding and generation within a single autoregressive fr...
Large Vision Language Models (LVLMs) have demonstrated remarkable capabilities in understanding and describing visual content, achieving state-of-th...
The prevalence of ocular illnesses is growing globally, presenting a substantial public health challenge. Early detection and timely intervention ar...
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparativ...
Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coher...
In this work, a new method for automatically extending Bounding Box (BB) and mask labels across different channels on multilens cameras is presented...
Anomaly detection from images captured using camera sensors is one of the mainstream applications at the industrial level. Particularly, it maintain...
Glaucoma is a leading cause of irreversible blindness worldwide, emphasizing the critical need for early detection and intervention. In this paper, ...
Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data w...