1 code implementation • 18 Apr 2024 • Andrei-Timotei Ardelean, Tim Weyrich
By identifying the anomalous regions with high fidelity, we can restrict our focus to those regions of interest; then, contrastive learning is employed to increase the separability of different anomaly types and reduce the intra-class variation.
1 code implementation • 13 Apr 2023 • Andrei-Timotei Ardelean, Tim Weyrich
We propose a novel method for Zero-Shot Anomaly Localization on textures.
1 code implementation • CVPR 2022 • Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev
We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time.
no code implementations • CVPR 2023 • Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin
We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks.
no code implementations • International Journal of Computers Communications & Control 2020 • Andrei-Timotei Ardelean, Lucian Mircea Sasu
Unlike previous approaches which require large datasets of a specific person for training, our approach may start from a scarce set of images, even from a single image.