no code implementations • 23 Apr 2024 • Deheng Zhang, Clara Fernandez-Labrador, Christopher Schroers
We also propose a semantic-aware nearest neighbor matching algorithm to improve the style transfer quality.
no code implementations • 12 Apr 2024 • Lucas Relic, Roberto Azevedo, Markus Gross, Christopher Schroers
Incorporating diffusion models in the image compression domain has the potential to produce realistic and detailed reconstructions, especially at extremely low bitrates.
no code implementations • 30 Oct 2023 • Zhaowei Gao, Mingyang Song, Christopher Schroers, Yang Zhang
Our proposed method supports bidirectional spatio-temporal information propagation across multiple scales to leverage information in both space and time.
no code implementations • 23 Mar 2023 • Manuel Kansy, Anton Raël, Graziana Mignone, Jacek Naruniec, Christopher Schroers, Markus Gross, Romann M. Weber
Face recognition models embed a face image into a low-dimensional identity vector containing abstract encodings of identity-specific facial features that allow individuals to be distinguished from one another.
no code implementations • 16 Mar 2023 • Mingyang Song, Yang Zhang, Tunç O. Aydın, Elham Amin Mansour, Christopher Schroers
To this end, we propose an effective generative model which utilizes clean features as guidance followed by noise injections into the network.
no code implementations • CVPR 2023 • Carlos Gomes, Roberto Azevedo, Christopher Schroers
This performance gap can be explained by the fact that current NVR methods: i) use architectures that do not efficiently obtain a compact representation of temporal and spatial information; and ii) minimize rate and distortion disjointly (first overfitting a network on a video and then using heuristic techniques such as post-training quantization or weight pruning to compress the model).
no code implementations • CVPR 2023 • Markus Plack, Karlis Martins Briedis, Abdelaziz Djelouah, Matthias B. Hullin, Markus Gross, Christopher Schroers
Through this error estimation, our method can produce even higher-quality intermediate frames using only a fraction of the time compared to a full rendering.
no code implementations • CVPR 2023 • Michael Bernasconi, Abdelaziz Djelouah, Farnood Salehi, Markus Gross, Christopher Schroers
This renders our model applicable for different types of data not seen during the training such as normals.
no code implementations • 7 Jan 2022 • Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah, Markus Gross, Christopher Schroers
Recently, significant progress has been made in learned image and video compression.
no code implementations • 9 Sep 2020 • Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah, Markus Gross, Christopher Schroers
In contrast to this, we propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation.
no code implementations • ICLR Workshop Neural_Compression 2021 • Leonhard Helminger, Abdelaziz Djelouah, Markus Gross, Christopher Schroers
However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization.
no code implementations • 4 Jun 2019 • Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah, Christopher Schroers
The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches.
no code implementations • NeurIPS 2019 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt
The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy.
no code implementations • 27 Sep 2018 • Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt
We propose a variational inference approach to deep probabilistic video compression.
no code implementations • 9 Aug 2018 • Simone Meyer, Victor Cornillère, Abdelaziz Djelouah, Christopher Schroers, Markus Gross
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames.
6 code implementations • 9 Apr 2018 • Yifan Wang, Federico Perazzi, Brian McWilliams, Alexander Sorkine-Hornung, Olga Sorkine-Hornung, Christopher Schroers
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality.
Ranked #14 on Image Super-Resolution on BSD100 - 4x upscaling
no code implementations • CVPR 2018 • Meng Tang, Abdelaziz Djelouah, Federico Perazzi, Yuri Boykov, Christopher Schroers
Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods.
no code implementations • CVPR 2018 • Simone Meyer, Abdelaziz Djelouah, Brian McWilliams, Alexander Sorkine-Hornung, Markus Gross, Christopher Schroers
We show that this is superior to the hand-crafted heuristics previously used in phase-based methods and also compares favorably to recent deep learning based approaches for video frame interpolation on challenging datasets.
no code implementations • ECCV 2018 • Meng Tang, Federico Perazzi, Abdelaziz Djelouah, Ismail Ben Ayed, Christopher Schroers, Yuri Boykov
This approach simplifies weakly-supervised training by avoiding extra MRF/CRF inference steps or layers explicitly generating full masks, while improving both the quality and efficiency of training.