Latest AI and machine learning research in surveys for healthcare professionals.
This paper examines how large language models (LLMs) are transforming core quantitative methods in communication research in particular, and in the social sciences more broadly-namely, content analysis, survey research, and experimental studies. Rather than replacing classical approaches, LLMs introduce new possibilities for coding and interpreting text, simulating dynamic respondents, and gener...
Text-to-Image (T2I) models have demonstrated impressive capabilities in generating high-quality and diverse visual content from natural language prompts. However, uncontrolled reproduction of sensitive, copyrighted, or harmful imagery poses serious ethical, legal, and safety challenges. To address these concerns, the concept erasure paradigm has emerged as a promising direction, enabling the sel...
Concept-based Models are a class of inherently explainable networks that improve upon standard Deep Neural Networks by providing a rationale behind ...
We investigate the existence and persistence of a specific type of gender bias in some of the popular LLMs and contribute a new benchmark dataset, R...
Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images....
The integration of large language model (LLM) and data management (DATA) is rapidly redefining both domains. In this survey, we comprehensively revi...
Person re-identification (ReID) models are known to suffer from camera bias, where learned representations cluster according to camera viewpoints ra...
Reliability and generalization in deep learning are predominantly studied in the context of image classification. Yet, real-world applications in sa...
The Internet of Things (IoT) and Large Language Models (LLMs) have been two major emerging players in the information technology era. Although there...
Despite the impressive performance of generative Diffusion Models (DMs), their internal working is still not well understood, which is potentially p...
Diffusion models have emerged as leading generative models for images and other modalities, but aligning their outputs with human preferences and sa...
The biases exhibited by text-to-image (TTI) models are often treated as independent, though in reality, they may be deeply interrelated. Addressing ...
In this work, we propose Dimple, the first Discrete Diffusion Multimodal Large Language Model (DMLLM). We observe that training with a purely discre...
Segment Anything Models (SAM) have achieved remarkable success in object segmentation tasks across diverse datasets. However, these models are predo...
Medical Visual Question Answering (MedVQA) is crucial for enhancing the efficiency of clinical diagnosis by providing accurate and timely responses ...
Due to the legal and ethical responsibilities of healthcare providers (HCPs) for accurate documentation and protection of patient data privacy, the ...
This paper presents BR-TaxQA-R, a novel dataset designed to support question answering with references in the context of Brazilian personal income t...
Bias in Large Language Models (LLMs) significantly undermines their reliability and fairness. We focus on a common form of bias: when two reference ...
Vision-Language Models (VLMs) have demonstrated impressive capabilities in understanding visual content, but their reliability in safety-critical co...
Personalizing 3D scenes from a single reference image enables intuitive user-guided editing, which requires achieving both multi-view consistency ac...