Suchergebnisse - "multi-modal variational autoencoder"

Dmvae: a dual-stream multi-modal variational autoencoder for multi-task fake news detection von Guo, Ying, Hu, Shuting, Li, Yao, Di, Chong, Liu, Jie

“… The proliferation of fake news on social media platforms, facilitated by the development of the Internet, has become a pressing social issue, intensifying the …”
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Anytime 3D Object Reconstruction Using Multi-Modal Variational Autoencoder von Yu, Hyeonwoo, Oh, Jean

“… For effective human-robot teaming, it is important for the robots to be able to share their visual perception with the human operators. In a harsh remote …”
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Time-Lag Aware Multi-Modal Variational Autoencoder Using Baseball Videos And Tweets For Prediction Of Important Scenes von Hirasawa, Kaito, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki

“… A novel method based on time-lag aware multi-modal variational autoencoder for prediction of important scenes (TI-MVAE-PIS …”
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A Markov Random Field Multi-Modal Variational AutoEncoder von Oubari, Fouad, Mohamed El Baha, Meunier, Raphael, Rodrigue Décatoire, Mougeot, Mathilde

“… Recent advancements in multimodal Variational AutoEncoders (VAEs) have highlighted their potential for modeling complex data from multiple modalities …”
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Human Emotion Estimation Using Multi-Modal Variational AutoEncoder with Time Changes von Moroto, Yuya, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki

“… A human emotion estimation method via feature integration using multi-modal variational autoencoder (MVAE …”
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Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder von Yu, Hyeonwoo, Oh, Jean

“… For effective human-robot teaming, it is important for the robots to be able to share their visual perception with the human operators. In a harsh remote …”
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A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing von Korthals, Timo, Schilling, Malte, Leitner, Jürgen

“… This contribution comprises the interplay between a multi-modal variational autoencoder and an environment to a perceived environment, on which an agent can act …”
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Combined Generation of Electrocardiogram and Cardiac Anatomy Models Using Multi-Modal Variational Autoencoders von Beetz, Marcel, Banerjee, Abhirup, Sang, Yuling, Grau, Vicente

“… In this work, we propose a novel multi-modal variational autoencoder (VAE) capable of processing combined physiology and bitemporal anatomy information in the form of electrocardiograms (ECG …”
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Rolling Bearing Fault Diagnosis Based on Multi-Modal Variational Autoencoders von Xiong, Manjun, Wu, Yifan, Li, Chuan, Yang, Zhe

“… For this reason, a multi-modal variational autoencoder (MMVAE) is proposed to extract useful features from multiple modalities …”
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Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Volume Deviations in Alzheimer's Disease von Kumar, Sayantan, Payne, Philip R O, Sotiras, Aristeidis

“… To address this limitation, we propose a multi-modal variational autoencoder (mmVAE) based normative modelling framework that can capture the joint distribution between different modalities to identify …”
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Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities von Ana Lawry Aguila, Chapman, James, Altmann, Andre

“… One of the challenges of studying common neurological disorders is disease heterogeneity including differences in causes, neuroimaging characteristics, …”
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Multi-Modal Domain Adaptation Variational Auto-encoder for EEG-Based Emotion Recognition von Yixin Wang, Shuang Qiu, Dan Li, Changde Du, Bao-Liang Lu, Huiguang He

“… data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common …”
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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition von Wang, Yixin, Qiu, Shuang, Li, Dan, Du, Changde, Lu, Bao-Liang, He, Huiguang

“… Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space …”
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Self-Supervised Audio-Visual Feature Learning for Single-modal Incremental Terrain Type Clustering von Ishikawa, Reina, Hachiuma, Ryo, Saito, Hideo

“… In this paper, we present a novel framework using the multi-modal variational autoencoder and the Gaussian mixture model clustering algorithm on image data and audio data for terrain …”
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Cross-modal Variational Alignment of Latent Spaces von Theodoridis, Thomas, Chatzis, Theocharis, Solachidis, Vassilios, Dimitropoulos, Kosmas, Daras, Petros

“… The first network is a multi modal variational autoencoder that maps directly one modality to the other, while the second one is a single-modal variational autoencoder …”
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Fake News Detection Using BERT-VGG19 Multimodal Variational Autoencoder von Jaiswal, Ramji, Singh, Upendra Pratap, Singh, Krishna Pratap

“… In this era of readily accessible Internet, there has been a monumental shift in the way information is created, processed and disseminated to the netizens …”
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Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations von Lee, Mihee, Pavlovic, Vladimir

“… In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities …”
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Bayesian structural model updating with multimodal variational autoencoder von Itoi, Tatsuya, Amishiki, Kazuho, Lee, Sangwon, Yaoyama, Taro

“… The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE …”
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Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem von Rodriguez, Oscar, Taylor, Jamie M, Pardo, David

“… However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture …”
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Geometry-informed multimodal variational autoencoder for real-time prediction of properties for Ti–6Al–4V fabricated using PBF-LB von Luo, Qixiang, Huang, Nancy, Bartles, Dean L., Beese, Allison M.

“… A geometry-informed multimodal variational autoencoder linear hybrid model (GMVAE) was developed to use in situ processing signals along with geometry information from laser scanning patterns to predict the mechanical properties of Ti-6Al-4 …”
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