Latest AI and machine learning research in cultural competence for healthcare professionals.
With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This review provides a state-of-the-art overview of recent advancements in AI technologies and their applications in enhancing cardiovascular health at the population level. From predictive analytics to personalized interventions, AI-driven approaches are i...
Personalizing 3D scenes from a single reference image enables intuitive user-guided editing, which requires achieving both multi-view consistency across perspectives and referential consistency with the input image. However, these goals are particularly challenging due to the viewpoint bias caused by the limited perspective provided in a single image. Lacking the mechanisms to effectively expand...
Algorithmic tools are increasingly used in hiring to improve fairness and diversity, often by enforcing constraints such as gender-balanced candidat...
Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and rec...
Multilingual vision-language models promise universal image-text retrieval, yet their social biases remain under-explored. We present the first syst...
Deep learning models often achieve high performance by inadvertently learning spurious correlations between targets and non-essential features. For ...
Coordinated multi-arm manipulation requires satisfying multiple simultaneous geometric constraints across high-dimensional configuration spaces, whi...
Few-shot cross-modal retrieval focuses on learning cross-modal representations with limited training samples, enabling the model to handle unseen cl...
How discriminative position information is for image classification depends on the data. On the one hand, the camera position is arbitrary and objec...
Diffusion distillation has emerged as a promising strategy for accelerating text-to-image (T2I) diffusion models by distilling a pretrained score ne...
Text-to-image generation models have achieved remarkable capabilities in synthesizing images, but often struggle to provide fine-grained control ove...
Few-shot classification of hyperspectral images (HSI) faces the challenge of scarce labeled samples. Self-Supervised learning (SSL) and Few-Shot Lea...
The memorization of sensitive and personally identifiable information (PII) by large language models (LLMs) poses growing privacy risks as models sc...
AI-enhanced personality assessments are increasingly shaping hiring decisions, using affective computing to predict traits from the Big Five (OCEAN)...
Data augmentation for domain-specific image classification tasks often struggles to simultaneously address diversity, faithfulness, and label clarit...
Algorithmic bias has been the subject of much recent controversy. To clarify what is at stake and to make progress resolving the controversy, a bett...
Despite recent advances in text-to-image generation, using synthetically generated data seldom brings a significant boost in performance for supervi...
Recent progress in Multimodal Large Language Models (MLLMs) have significantly enhanced the ability of artificial intelligence systems to understand...
Imagine hearing a dog bark and turning toward the sound only to see a parked car, while the real, silent dog sits elsewhere. Such sensory conflicts ...
Deep vision models often rely on biases learned from spurious correlations in datasets. To identify these biases, methods that interpret high-level,...