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IEEE Access, 2023 Multimodal learning has gained immense popularity due to the explosive growth in the volume of image and textual data in various domains. Vision-language heterogeneous multimodal data has been utilized to solve a variety of tasks including classification,
Priyankar Bose +2 more
doaj +1 more source
Robust Multimodal Dictionary Learning [PDF]
, 2013 We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities.
Tian Cao 0001 +5 more
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Embodied Multimodal Multitask Learning [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Visually-grounded embodied language learning models have recently shown to be effective at learning multiple multimodal tasks such as following navigational instructions and answering questions. In this paper, we address two key limitations of these models, (a) the inability to transfer the grounded knowledge across different tasks and (b) the ...
Devendra Singh Chaplot +4 more
openaire +3 more sources
The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences [PDF]
, 2020 This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing.
Di Mitri, D.
core +1 more source
Designing multimodal classrooms for social justice [PDF]
, 2014 This is an Accepted Manuscript of an article published by Taylor & Francis in Classroom Discourse on 24 Jan 2014, available online: http://www.tandfonline.com/10.1080/19463014.2013.859842.This paper explores the ways in which multimodal classroom ...
Archer, Arlene
core +1 more source
From signals to knowledge: A conceptual model for multimodal learning analytics
, 2018 Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods.
Di Mitri, D. +12 more
core +1 more source
Multimodal ways of eliciting students' voice. [PDF]
, 2010 For some time researchers have been exploring how students might best be supported to express their ideas, opinions and feelings and to demonstrate what they know and can do.
Cowie, Bronwen +2 more
core +1 more source
Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models
Frontiers in Computer Science, 2021 We propose a method for multimodal concept formation. In this method, unsupervised multimodal clustering and cross-modal inference, as well as unsupervised representation learning, can be performed by integrating the multimodal latent Dirichlet ...
Ryo Kuniyasu +4 more
doaj +1 more source
Factorized Multimodal Transformer for Multimodal Sequential Learning
CoRR, 2019 The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental research area.
Amir Zadeh 0001 +6 more
openaire +3 more sources
Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
wiley +1 more source