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Found 5 papers, 1 papers with code
Learning Visual Styles from Audio-Visual Associations
Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization.
Neural Dubber: Dubbing for Videos According to Scripts
Neural Dubber is a multi-modal text-to-speech (TTS) model that utilizes the lip movement in the video to control the prosody of the generated speech.
Modality Laziness: Everybody's Business is Nobody's Business
We name this problem of multi-modal training, \emph{Modality Laziness}.
Improving Multi-Modal Learning with Uni-Modal Teachers
We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.
Ranked #33 on
Semantic Segmentation
on NYU Depth v2
Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation
Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation.
Ranked #20 on
Music Source Separation
on MUSDB18
