From West Point classrooms to ROTC flight simulators, AI-powered study tools are transforming how cadets prepare for missions, master languages, and engage with complex concepts. These tools offer ...

Deep Multimodal Learning with Missing Modality: A Survey TMLR 2026 Paper N/A Multimodal Learning Under Imperfect Data Conditions: A Survey arxiv 2026 Paper N/A Multimodal fusion on low-quality data: A ...

This study investigated how Chinese learners of English perceive the effectiveness of different multimodal input for vocabulary learning. Forty participants perceived 14 combinations of visual, ...

Abstract: This systematic literature review explores the application of multimodal learning analysis (MMLA), with physiological signals collected through wearable devices as the primary data source, ...

The integration of multi-modal learning with large-scale models has become a transformative force in the fields of artificial intelligence and neurorobotics. Human perception naturally relies on the ...

This study presents a valuable application of a video-text alignment deep neural network model to improve neural encoding of naturalistic stimuli in fMRI. The authors provide convincing evidence that ...

Abstract: The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continues to ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

1 Department of Neuroscience, Institute of Psychopathology, Rome, Italy. 2 Department of Computer Engineering (AI), University of Genova, Genova, Italy. Cognitive impairment is a frequent and ...

The PlantIF framework consists of image and text feature extractors, semantic space encoders, and a multimodal feature fusion module. Image and text feature extractors are used to present visual and ...