Results 1 to 10 of about 6,586,139 (300)
Multimodal learning with graphs. [PDF]
Nat Mach Intell, 2023 Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of assumptions that algorithms use to
Ektefaie Y +4 more
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Multimodal Learning With Transformers: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023 This paper is accepted by IEEE ...
Peng Xu, Xiatian Zhu, David Clifton
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Deep Multimodal Representation Learning: A Survey
IEEE Access, 2019 Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization of ubiquitous multimodal data.
Wenzhong Guo, Jianwen Wang, Shiping Wang
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Bio‐Plausible Multimodal Learning with Emerging Neuromorphic Devices [PDF]
Advanced ScienceMultimodal machine learning, as a prospective advancement in artificial intelligence, endeavors to emulate the brain's multimodal learning abilities with the objective to enhance interactions with humans.
Haonan Sun +7 more
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Editorial: Advances in multimodal learning: pedagogies, technologies, and analytics [PDF]
Frontiers in Psychology, 2023 Heng Luo
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Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach
Frontiers in Computer Science, 2022 We tackle the task of cross-domain visual geo-localization, where the goal is to geo-localize a given query image against a database of geo-tagged images, in the case where the query and the database belong to different visual domains.
Valerio Paolicelli +5 more
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A Survey on Deep Visual Place Recognition
IEEE Access, 2021 In recent years visual place recognition (VPR), i.e., the problem of recognizing the location of images, has received considerable attention from multiple research communities, spanning from computer vision to robotics and even machine learning.
Carlo Masone, Barbara Caputo
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Calibrating Multimodal Learning
CoRR, 2023 Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial ...
Huan Ma 0006 +6 more
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CoRR, 2023 This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually. Further, modeling frameworks are discussed where one modality is transformed into the other, as well as models in
Cem Akkus +16 more
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Multimodality as universality: Designing inclusive accessibility to graphical information
Frontiers in Education, 2023 Graphical representations are ubiquitous in the learning and teaching of science, technology, engineering, and mathematics (STEM). However, these materials are often not accessible to the over 547,000 students in the United States with blindness and ...
Stacy A. Doore +6 more
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