To acquire robust representations in an unsupervised manner, regular self-supervised contrastive learning trains neural networks to make the feature representation of a sample close to those of its computationally transformed versions.
To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance.
In order to analyze the motion of lyric words, we first apply a state-of-the-art scene text detector and recognizer to each video frame.
To help users respond to plane-search queries, we also propose using a gallery-based interface that provides options in the two-dimensional subspace arranged in an adaptive grid view.
Specifically, we formulate a hierarchical generative model of poses and images by integrating a deep generative model of poses from pose features with that of images from poses and image features.
This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody.
Third, we carried out qualitative experiments and showed that taking addiction into account enables us to analyze music listening behavior from a new viewpoint in terms of how people listen to music according to the time of day, how an artist's songs are listened to by people, etc.
This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics.