We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts.
Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects.
no code implementations • 10 Sep 2020 • Akhmedkhan Shabanov, Ilya Krotov, Nikolay Chinaev, Vsevolod Poletaev, Sergei Kozlukov, Igor Pasechnik, Bulat Yakupov, Artsiom Sanakoyeu, Vadim Lebedev, Dmitry Ulyanov
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification.
Recent work has significantly improved the representation of color and texture and computational speed and image resolution.
Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.
Approaches for learning a single distance metric often struggle to encode all different types of relationships and do not generalize well.
Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention.
These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision.
Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes.