Latest AI and machine learning research in ophthalmology for healthcare professionals.
This study investigates how artificial intelligence (AI) recognizes style through style transfer-an AI technique that generates a new image by applying the style of one image to another. Despite the considerable interest that style transfer has garnered among researchers, most efforts have focused on enhancing the quality of output images through advanced AI algorithms. In this paper, we approac...
The challenge of tracing the source attribution of forged faces has gained significant attention due to the rapid advancement of generative models. However, existing deepfake attribution (DFA) works primarily focus on the interaction among various domains in vision modality, and other modalities such as texts and face parsing are not fully explored. Besides, they tend to fail to assess the gener...
We present an autonomous aerial surveillance platform, Veg, designed as a fault-tolerant quadcopter system that integrates visual SLAM for GPS-indep...
Logit Lens is a widely adopted method for mechanistic interpretability of transformer-based language models, enabling the analysis of how internal r...
The purpose of this study was to develop and evaluate a novel virtual reality seated orientation and mobility (VR-S-O&M) test protocol designed to a...
Vision-language models (VLMs) have demonstrated impressive capabilities in understanding and reasoning about visual and textual content. However, th...
Medical Large Vision-Language Models (Med-LVLMs) demonstrate significant potential in healthcare, but their reliance on general medical data and coa...
Accurate retinal vessel segmentation provides essential structural information for ophthalmic image analysis. However, existing methods struggle wit...
Inspired by the data-efficient spiking mechanism of neurons in the human eye, event cameras were created to achieve high temporal resolution with mi...
Neural networks that map between low dimensional spaces are ubiquitous in computer graphics and scientific computing; however, in their naive implem...
In this study, we address the challenges associated with accurately determining gaze location on a screen, which is often compromised by noise from ...
Suicide is a critical global public health issue, with millions experiencing suicidal ideation (SI) each year. Online spaces enable individuals to e...
We introduce Perception Encoder (PE), a state-of-the-art vision encoder for image and video understanding trained via simple vision-language learnin...
In recent years, the field of vision-language model pre-training has experienced rapid advancements, driven primarily by the continuous enhancement ...
Recent advances in reinforcement learning (RL) have strengthened the reasoning capabilities of vision-language models (VLMs). However, enhancing pol...
Recent advances in reinforcement learning (RL) have strengthened the reasoning capabilities of vision-language models (VLMs). However, enhancing pol...
Domain generalization aims at training on source domains to uncover a domain-invariant feature space, allowing the model to perform robust generaliz...
Vision Foundation Models (VFMs) have delivered remarkable performance in Domain Generalized Semantic Segmentation (DGSS). However, recent methods of...
Timely and accurate detection of hurricane debris is critical for effective disaster response and community resilience. While post-disaster aerial i...
Road surface is the sole contact medium for wheels or robot feet. Reconstructing road surface is crucial for unmanned vehicles and mobile robots. Re...