1 code implementation • 14 Oct 2023 • Wojciech Kozłowski, Michał Szachniewicz, Michał Stypułkowski, Maciej Zięba
To fill this gap, we propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs to replicate scenes captured under extreme lighting conditions taken by that specific camera.
1 code implementation • 11 Jul 2023 • Michał Szachniewicz, Wojciech Kozłowski, Michał Stypułkowski, Maciej Zięba
We introduce PointCAM, a novel adversarial method for learning a masking function for point clouds.
no code implementations • 10 Jan 2023 • Dan Bigioi, Shubhajit Basak, Michał Stypułkowski, Maciej Zięba, Hugh Jordan, Rachel McDonnell, Peter Corcoran
Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model.
no code implementations • 6 Jan 2023 • Michał Stypułkowski, Konstantinos Vougioukas, Sen He, Maciej Zięba, Stavros Petridis, Maja Pantic
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos.
no code implementations • 16 May 2022 • Maciej Zamorski, Michał Stypułkowski, Konrad Karanowski, Tomasz Trzciński, Maciej Zięba
By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
1 code implementation • 22 Jun 2021 • Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, Adrian Łańcucki, Ricard Marxer, Mateusz Opala, Piotr Pusz, Paweł Rychlikowski, Michał Stypułkowski
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021.
1 code implementation • 24 Apr 2021 • Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, Adrian Łańcucki, Ricard Marxer, Mateusz Opala, Piotr Pusz, Paweł Rychlikowski, Michał Stypułkowski
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations.
1 code implementation • 7 Oct 2020 • Michał Stypułkowski, Kacper Kania, Maciej Zamorski, Maciej Zięba, Tomasz Trzciński, Jan Chorowski
To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.
2 code implementations • 16 Oct 2019 • Michał Stypułkowski, Maciej Zamorski, Maciej Zięba, Jan Chorowski
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.