1 code implementation • ECCV 2020 • Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, Victor Lempitsky
Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.
7 code implementations • 15 Sep 2021 • Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Ranked #3 on
Seeing Beyond the Visible
on KITTI360-EX
1 code implementation • 21 Aug 2020 • Elizaveta Logacheva, Roman Suvorov, Oleg Khomenko, Anton Mashikhin, Victor Lempitsky
Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way.
no code implementations • 27 Nov 2018 • Pavel Solovev, Vladimir Aliev, Pavel Ostyakov, Gleb Sterkin, Elizaveta Logacheva, Stepan Troeshestov, Roman Suvorov, Anton Mashikhin, Oleg Khomenko, Sergey I. Nikolenko
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors.
no code implementations • 19 Nov 2018 • Pavel Ostyakov, Roman Suvorov, Elizaveta Logacheva, Oleg Khomenko, Sergey I. Nikolenko
We present a novel approach to image manipulation and understanding by simultaneously learning to segment object masks, paste objects to another background image, and remove them from original images.
no code implementations • 12 Sep 2018 • Pavel Ostyakov, Elizaveta Logacheva, Roman Suvorov, Vladimir Aliev, Gleb Sterkin, Oleg Khomenko, Sergey I. Nikolenko
Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge.