Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Artsiom Sanakoyeu"'
Publikováno v:
IEEE transactions on pattern analysis and machine intelligence. 44(11)
Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another
Publikováno v:
CVPR
We tackle the problem of learning the geometry of multiple categories of deformable objects jointly. Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects. However, training such m
Autor:
Igor Pasechnik, Sergei Kozlukov, Nikolay Chinaev, Vsevolod Poletaev, Artsiom Sanakoyeu, Ilya Krotov, Bulat Yakupov, Vadim Lebedev, Akhmedkhan Shabanov, Dmitry Ulyanov
Publikováno v:
3DV
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification. However, the quality of the captured depth is sometimes insufficient for 3D reconstruction, tracking and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac2d8be0b757eeb0126ff0d1628754b6
Publikováno v:
CVPR
Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18703802bce81b240e267ebfbf3c84f3
Publikováno v:
CVPR 2020
CVPR
CVPR
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. In principle, the same approach could be extended to any animal class, but the effor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f031c3f9de1fabdfb920931692694a63
https://hdl.handle.net/21.11116/0000-0008-2E23-E21.11116/0000-0008-2E25-C
https://hdl.handle.net/21.11116/0000-0008-2E23-E21.11116/0000-0008-2E25-C
Publikováno v:
ICCV
Artists rarely paint in a single style throughout their career. More often they change styles or develop variations of it. In addition, artworks in different styles and even within one style depict real content differently: while Picasso's Blue Perio
Publikováno v:
CVPR
Learning the embedding space, where semantically similar objects are located close together and dissimilar objects far apart, is a cornerstone of many computer vision applications. Existing approaches usually learn a single metric in the embedding sp
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030336752
GCPR
GCPR
Seismic image analysis plays a crucial role in a wide range of industrial applications and has been receiving significant attention. One of the essential challenges of seismic imaging is detecting subsurface salt structure which is indispensable for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::532c8db0ee344e28a3bddcc941f38a15
https://doi.org/10.1007/978-3-030-33676-9_15
https://doi.org/10.1007/978-3-030-33676-9_15
Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52f458fe9b0769cb965c0e8d4a3f67fa
http://arxiv.org/abs/1802.08562
http://arxiv.org/abs/1802.08562
Publikováno v:
Computer Vision – ECCV 2018 ISBN: 9783030012366
ECCV (8)
ECCV (8)
Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ad00fb0cbaf233160d3177f25716f63
https://doi.org/10.1007/978-3-030-01237-3_43
https://doi.org/10.1007/978-3-030-01237-3_43