Latest AI and machine learning research in cultural competence for healthcare professionals.
Domain Generalized Semantic Segmentation (DGSS) seeks to utilize source domain data exclusively to enhance the generalization of semantic segmentation across unknown target domains. Prevailing studies predominantly concentrate on feature normalization and domain randomization, these approaches exhibit significant limitations. Feature normalization-based methods tend to confuse semantic features ...
This study investigates the trade-offs between fairness, privacy, and utility in image classification using machine learning (ML). Recent research suggests that generalization techniques can improve the balance between privacy and utility. One focus of this work is sharpness-aware training (SAT) and its integration with differential privacy (DP-SAT) to further improve this balance. Additionally,...
Objectives: Compare qualitative coding of instruction tuned large language models (IT-LLMs) against human coders in classifying the presence or abse...
Existing work on large language model (LLM) personalization assigned different responding roles to LLM, but overlooked the diversity of questioners....
Data-free knowledge distillation transfers knowledge by recovering training data from a pre-trained model. Despite the recent success of seeking glo...
Most of the ML datasets we use today are biased. When we train models on these biased datasets, they often not only learn dataset biases but can als...
Kinship face synthesis is a challenging problem due to the scarcity and low quality of the available kinship data. Existing methods often struggle t...
Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to ...
As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. However, w...
Bias significantly undermines both the accuracy and trustworthiness of machine learning models. To date, one of the strongest biases observed in ima...
Machine learning (ML) algorithms have become integral to decision making in various domains, including healthcare, finance, education, and law enfor...
This study seeks to automate camera movement control for filming existing subjects into attractive videos, contrasting with the creation of non-exis...
The rise of digital platforms has led to an increasing reliance on technology-driven, home-based healthcare solutions, enabling individuals to monit...
Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analys...
The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platf...
As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These m...
Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term ...
While one commonly trains large diffusion models by collecting datasets on target downstream tasks, it is often desired to align and finetune pretra...
With the rapid development of multimedia, the shift from unimodal textual sentiment analysis to multimodal image-text sentiment analysis has obtaine...
Score distillation of 2D diffusion models has proven to be a powerful mechanism to guide 3D optimization, for example enabling text-based 3D generat...