A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the ...

A new computational model of the brain based closely on its biology and physiology has not only learned a simple visual ...

Background Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains ...

Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...

Abstract: Audio-visual zero-shot learning (ZSL) leverages both video and audio information for model training, aiming to classify new video categories that were not seen during the training. However, ...

This paper tackles the domain of multimodal prompting for visual recognition, specifically when dealing with missing modalities through multimodal Transformers. It presents two main contributions: (i) ...

ABSTRACT: Many institutions of higher learning have been, and in some cases continue to be, profoundly shaped by the effects of the COVID-19 pandemic. The University of Namibia (UNAM) was not exempt ...

Abstract: This paper introduces AVCaps, an audio-visual dataset that contains separate textual captions for the audio, visual, and audio-visual contents of video clips. The dataset contains 2061 video ...

1 Department of Otolaryngology Head and Neck Surgery, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China 2 State Key Laboratory of Cognitive ...

Multimodal artificial intelligence faces fundamental challenges in effectively integrating and processing diverse data types simultaneously. Current methodologies predominantly rely on late-fusion ...