Latest AI and machine learning research in cultural competence for healthcare professionals.
Deep generative models are proficient in generating realistic data but struggle with producing rare samples in low density regions due to their scarcity of training datasets and the mode collapse problem. While recent methods aim to improve the fidelity of generated samples, they often reduce diversity and coverage by ignoring rare and novel samples. This study proposes a novel approach for gene...
Person re-identification (ReID) models often struggle to generalize across diverse cultural contexts, particularly in Islamic regions like Iran, where modest clothing styles are prevalent. Existing datasets predominantly feature Western and East Asian fashion, limiting their applicability in these settings. To address this gap, we introduce IUST_PersonReId, a dataset designed to reflect the uniq...
The use of CLIP embeddings to assess the alignment of samples produced by text-to-image generative models has been extensively explored in the liter...
Latent Diffusion Models (LDMs) produce high-quality, photo-realistic images, however, the latency incurred by multiple costly inference iterations c...
Large language models (LLMs) have brought exciting new advances to mobile UI agents, a long-standing research field that aims to complete arbitrary ...
Retrieval augmentation, the practice of retrieving additional data from large auxiliary pools, has emerged as an effective technique for enhancing m...
Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public...
Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases. However, a significant limitation of ...
Generative AI models like GPT-4o and DALL-E 3 are reshaping digital content creation, offering industries tools to generate diverse and sophisticate...
Recently, 3D generative domain adaptation has emerged to adapt the pre-trained generator to other domains without collecting massive datasets and ca...
Robust Principal Component Analysis (RPCA) is a fundamental technique for decomposing data into low-rank and sparse components, which plays a critic...
Considering the difficulty of interpreting generative model output, there is significant current research focused on determining meaningful evaluati...
We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (V...
The paper presents a novel Wi-Fi fingerprinting system that uses Channel State Information (CSI) data for fine-grained pedestrian localization. The ...
CNNs have become one of the most commonly used computational tool in the past two decades. One of the primary downsides of CNNs is that they work as...
Large language models (LLMs) have shown promising capabilities in healthcare analysis but face several challenges like hallucinations, parroting, an...
Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize w...
This paper proposes a novel interdisciplinary framework for the critical evaluation of text-to-image models, addressing the limitations of current t...
Generalized Category Discovery is a significant and complex task that aims to identify both known and undefined novel categories from a set of unlab...
Software engineering (SE) faces significant diversity challenges in both academia and industry, with underrepresented students encountering hostile ...