In this paper, we propose a novel cross-modal variational alignment method in order to process and relate information across different modalities. The proposed approach consists of two variational autoencoder (VAE) networks which generate and model the latent space of each modality.
In this paper, we propose a novel cross-modal varia- tional alignment method in order to process and relate in- formation across different modalities.
In this paper, we propose a novel cross-modal varia- tional alignment method in order to process and relate in- formation across different modalities.
In this paper, we propose a novel cross-modal varia-tional alignment method in order to process and relate information across different modalities.
In this paper, we propose a novel cross-modal variational alignment method in order to process and relate information across different modalities.
This work proposes a hierarchical Bayesian deep learning model, dubbed mismatch localization variational autoencoder (ML-VAE), which decomposes the ...
Variants of VAEs have gained significant traction in the scientific literature on modality integration as they can capture non-linear and complex data ...
Dec 9, 2024 · We address this challenge by designing a model that aligns audio-visual modalities by enriching audio features with visual information and ...
Hence, in this work, we train VAEs to encode and decode features from different modalities, and align their latent spaces by matching the parametrized latent.
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The resulting cross-modal latent space preserves facial identity, producing more visually appealing and higher fidelity avatars than previous methods, as ...
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