Latest AI and machine learning research in ophthalmology for healthcare professionals.
Automated radiology report generation aims to expedite the tedious and error-prone reporting process for radiologists. While recent works have made progress, learning to align medical images and textual findings remains challenging due to the relative scarcity of labeled medical data. For example, datasets for this task are much smaller than those used for image captioning in computer vision. In...
Face Emotion Recognition (FER) is essential for social interactions and understanding others' mental states. Utilizing eye tracking to investigate FER has yielded insights into cognitive processes. In this study, we utilized an instructionless paradigm to collect eye movement data from 21 participants, examining two FER processes: free viewing and grounded FER. We analyzed fixational, pupillary,...
To deepen our understanding of optical astronomy, we must advance imaging technology to overcome conventional frame-based cameras' limited dynamic r...
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are rel...
Depth imaging is a foundational building block for broad applications, such as autonomous driving and virtual/augmented reality. Traditionally, dept...
The recent successes of Vision-Language models raise the question of how to equivalently imbue a pretrained speech model with vision understanding, ...
Despite the recent success of image-text contrastive models like CLIP and SigLIP, these models often struggle with vision-centric tasks that demand ...
We introduce Visual Persona, a foundation model for text-to-image full-body human customization that, given a single in-the-wild human image, genera...
The proliferation of synthetic images generated by advanced AI models poses significant challenges in identifying and understanding manipulated visu...
Text-and-Image-To-Image (TI2I), an extension of Text-To-Image (T2I), integrates image inputs with textual instructions to enhance image generation. ...
While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying...
Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically ...
Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation...
Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Grap...
Current vision-language models (VLMs) have demonstrated remarkable capabilities in understanding multimodal data, but their potential remains undere...
Accurate emotion recognition is pivotal for nuanced and engaging human-computer interactions, yet remains difficult to achieve, especially in dynami...
As deep neural networks(DNN) become increasingly prevalent, particularly in high-stakes areas such as autonomous driving and healthcare, the ability...
ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special in...
Camera-to-robot (also known as eye-to-hand) calibration is a critical component of vision-based robot manipulation. Traditional marker-based methods...
Various Vision Transformer (ViT) models have been widely used for image recognition tasks. However, existing visual explanation methods can not disp...