Zobrazeno 1 - 10
of 157
pro vyhledávání: '"P. Stypulkowski"'
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the model to a specific domain, character, style, or concept. However, due
Externí odkaz:
http://arxiv.org/abs/2410.03941
Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations and limited access to photos with ground-truth lighting conditions. The latter is a crucial factor for supervised methods that ac
Externí odkaz:
http://arxiv.org/abs/2310.09633
Self-supervised methods have been proven effective for learning deep representations of 3D point cloud data. Although recent methods in this domain often rely on random masking of inputs, the results of this approach can be improved. We introduce Poi
Externí odkaz:
http://arxiv.org/abs/2307.05325
Autor:
Bigioi, Dan, Basak, Shubhajit, Stypułkowski, Michał, Zięba, Maciej, Jordan, Hugh, McDonnell, Rachel, Corcoran, Peter
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. Given a video of a talking person, and a separate auditor
Externí odkaz:
http://arxiv.org/abs/2301.04474
Autor:
Stypułkowski, Michał, Vougioukas, Konstantinos, He, Sen, Zięba, Maciej, Petridis, Stavros, Pantic, Maja
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stabl
Externí odkaz:
http://arxiv.org/abs/2301.03396
Contemporary deep neural networks offer state-of-the-art results when applied to visual reasoning, e.g., in the context of 3D point cloud data. Point clouds are important datatype for precise modeling of three-dimensional environments, but effective
Externí odkaz:
http://arxiv.org/abs/2205.08013
Autor:
Chorowski, Jan, Ciesielski, Grzegorz, Dzikowski, Jarosław, Łańcucki, Adrian, Marxer, Ricard, Opala, Mateusz, Pusz, Piotr, Rychlikowski, Paweł, Stypułkowski, Michał
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021. We build on the unsupervised representations of speech proposed by the organizers as a baseline, derived from CPC and clustered with the k-means a
Externí odkaz:
http://arxiv.org/abs/2106.11603
Autor:
Chorowski, Jan, Ciesielski, Grzegorz, Dzikowski, Jarosław, Łańcucki, Adrian, Marxer, Ricard, Opala, Mateusz, Pusz, Piotr, Rychlikowski, Paweł, Stypułkowski, Michał
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations. Rather than producing individual predictions for each of the future representations, the m
Externí odkaz:
http://arxiv.org/abs/2104.11946
Autor:
Stypułkowski, Michał, Kania, Kacper, Zamorski, Maciej, Zięba, Maciej, Trzciński, Tomasz, Chorowski, Jan
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of points and
Externí odkaz:
http://arxiv.org/abs/2010.11087
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