Latest AI and machine learning research in medicare for healthcare professionals.
Political polarization is a significant issue in American politics, influencing public discourse, policy, and consumer behavior. While studies on polarization in news media have extensively focused on verbal content, non-verbal elements, particularly visual content, have received less attention due to the complexity and high dimensionality of image data. Traditional descriptive approaches often ...
Classifying images with an interpretable decision-making process is a long-standing problem in computer vision. In recent years, Prototypical Part Networks has gained traction as an approach for self-explainable neural networks, due to their ability to mimic human visual reasoning by providing explanations based on prototypical object parts. However, the quality of the explanations generated by ...
Long video generation remains a challenging and compelling topic in computer vision. Diffusion based models, among the various approaches to video g...
Visual instructions for long-horizon tasks are crucial as they intuitively clarify complex concepts and enhance retention across extended steps. Dir...
Methods to quantify uncertainty in predictions from arbitrary models are in demand in high-stakes domains like medicine and finance. Conformal predi...
In personalized technology and psychological research, precisely detecting demographic features and personality traits from digital interactions bec...
Long Video Question Answering (LVQA) is challenging due to the need for temporal reasoning and large-scale multimodal data processing. Existing meth...
Recently, Probabilistic Language-Image Pre-Training (ProLIP) has been proposed to tackle the multiplicity issue of vision-language (VL) tasks. Despi...
From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understandin...
Transformer-based methods have demonstrated impressive performance in low-level visual tasks such as Image Super-Resolution (SR). However, its compu...
Smart factories enhance production efficiency and sustainability, but emergencies like human errors, machinery failures and natural disasters pose s...
Large language models (LLMs) are increasingly adopted in medical question-answering (QA) scenarios. However, LLMs can generate hallucinations and no...
Imitation learning frameworks for robotic manipulation have drawn attention in the recent development of language model grounded robotics. However, ...
We address the long-horizon mapless navigation problem: enabling robots to traverse novel environments without relying on high-definition maps or pr...
Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuros...
As research in the Scientometric deepens, the impact of data quality on research outcomes has garnered increasing attention. This study, based on We...
Advancing AI in computational pathology requires large, high-quality, and diverse datasets, yet existing public datasets are often limited in organ ...
Image quantization is a crucial technique in image generation, aimed at learning a codebook that encodes an image into a discrete token sequence. Re...
Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI)...
Generating knowledge-intensive and comprehensive long texts, such as encyclopedia articles, remains significant challenges for Large Language Models...