Results 51 to 60 of about 484,281 (309)
Dynamic Multimodal Fusion [PDF]
arXiv, 2022 Deep multimodal learning has achieved great progress in recent years. However, current fusion approaches are static in nature, i.e., they process and fuse multimodal inputs with identical computation, without accounting for diverse computational demands of different multimodal data.
arxiv
UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning [PDF]
arXiv, 2023 Multimodal learning aims to imitate human beings to acquire complementary information from multiple modalities for various downstream tasks. However, traditional aggregation-based multimodal fusion methods ignore the inter-modality relationship, treat each modality equally, suffer sensor noise, and thus reduce multimodal learning performance.
arxiv
Multimodal stationary states under Cauchy noise
, 2019 A L\'evy noise is an efficient description of out-of-equilibrium systems. The presence of L\'evy flights results in a plenitude of noise-induced phenomena.
Capała, Karol +2 more
core +1 more source
Multimodality imaging in cardiac amyloidosis: a primer for cardiologists.
European Heart Journal-Cardiovascular Imaging, 2020 Amyloidosis is a systemic infiltrative disease, in which unstable proteins misfold, form aggregates and amyloid fibrils which can deposit in various organs: heart, kidneys, liver, gastrointestinal tract, nervous system structures, lungs, or soft tissue ...
R. Jurcut +6 more
semanticscholar +1 more source
Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt [PDF]
arXiv, 2023 We have witnessed the rapid proliferation of multimodal data on numerous social media platforms. Conventional studies typically require massive labeled data to train models for Multimodal Aspect-Based Sentiment Analysis (MABSA). However, collecting and annotating fine-grained multimodal data for MABSA is tough.
arxiv
Human Brain Mapping, 2018 In this article, the authors aim to maximally utilize multimodality neuroimaging and genetic data for identifying Alzheimer's disease (AD) and its prodromal status, Mild Cognitive Impairment (MCI), from normal aging subjects.
Tao Zhou +3 more
semanticscholar +1 more source
On the Computational Benefit of Multimodal Learning [PDF]
arXiv, 2023 Human perception inherently operates in a multimodal manner. Similarly, as machines interpret the empirical world, their learning processes ought to be multimodal. The recent, remarkable successes in empirical multimodal learning underscore the significance of understanding this paradigm.
arxiv
Evidence of New Magnetic Transitions in Late-Type Dwarfs from Gaia DR2 [PDF]
, 2019 The second Gaia data release contains the identification of 147 535 low-mass ($\le 1.4 M_{\odot}$) rotational modulation variable candidates on (or close to) the main sequence, together with their rotation period and modulation amplitude.
Barnes, Sydney A. +3 more
core +2 more sources
Multimodal interactions in insect navigation [PDF]
, 2020 Animals travelling through the world receive input from multiple sensory modalities that could be important for the guidance of their journeys. Given the availability of a rich array of cues, from idiothetic information to input from sky compasses and ...
Buehlmann, Cornelia +2 more
core +2 more sources
Cardiovascular Imaging in Infective Endocarditis: A Multimodality Approach.
Circulation Cardiovascular Imaging, 2020 Multimodality imaging plays a pivotal role in the evaluation and management of infective endocarditis (IE)-a condition with high morbidity and mortality.
S. Horgan, Anuj Mediratta, L. Gillam
semanticscholar +1 more source