Latest AI and machine learning research in cultural competence for healthcare professionals.
Clothes-Changing Re-Identification (CC-ReID) aims to recognize individuals across different locations and times, irrespective of clothing. Existing methods often rely on additional models or annotations to learn robust, clothing-invariant features, making them resource-intensive. In contrast, we explore the use of color - specifically foreground and background colors - as a lightweight, annotati...
Result diversification (RD) is a crucial technique in Text-to-Image Retrieval for enhancing the efficiency of a practical application. Conventional methods focus solely on increasing the diversity metric of image appearances. However, the diversity metric and its desired value vary depending on the application, which limits the applications of RD. This paper proposes a novel task called CDR-CA (...
We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,00...
Open vocabulary Human-Object Interaction (HOI) detection is a challenging
task that detects all
Diffusion models has underpinned much recent advances of dataset augmentation in various computer vision tasks. However, when involving generating m...
Despite the remarkable progress of large language models (LLMs) across various domains, their capacity to predict retinopathy of prematurity (ROP) r...
Domain-specific image generation aims to produce high-quality visual content for specialized fields while ensuring semantic accuracy and detail fide...
Fourier ptychography (FP) is a powerful light-based synthetic aperture imaging technique that allows one to reconstruct a high-resolution, wide fiel...
Recent progress in large-scale reinforcement learning (RL) has notably enhanced the reasoning capabilities of large language models (LLMs), especial...
Current diversification strategies for text-to-image (T2I) models often ignore contextual appropriateness, leading to over-diversification where dem...
Current diversification strategies for text-to-image (T2I) models often ignore contextual appropriateness, leading to over-diversification where dem...
Artificial intelligence systems, especially those using machine learning, are being deployed in domains from hiring to loan issuance in order to aut...
MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity o...
Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribut...
Humans are able to recognize objects based on both local texture cues and the configuration of object parts, yet contemporary vision models primaril...
Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion ...
This narrative review explores the transformative role of artificial intelligence (AI) in forensic mental health, focusing on its applications, benefi...
Graph Neural Networks (GNNs) have been widely adopted to mine topological patterns contained in physiological signals for emotion recognition. However...
This paper studies the influence of behavioral biases on Fintech adoption. Additionally, the role of financial literacy in adaptation of Fintech servi...
The use of Natural Language Processing (NLP) in highstakes AI-based applications has increased significantly in recent years, especially since the e...