Author details
Author's Published Works
Scheduled Maintenance on Monday 1/13/2025
Single article sales and account management will be unavailable from 5:00 AM - 7:00 PM ET (09:00 - 23:00 UTC). We apologize for the inconvenience.
Author details
Showing 1-12 of 12 results
- Sort
Sort By
Results
Frame Interpolation Transformer and Uncertainty Guidance
Markus Plack;Matthias B. Hullin;Karlis Martins Briedis;Markus Gross;Abdelaziz Djelouah;Christopher Schroers
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or transformers. Still, the problem remains unsolved in more challenging conditions such...Show More
Kernel Aware Resampler
Michael Bernasconi;Abdelaziz Djelouah;Farnood Salehi;Markus Gross;Christopher Schroers
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Deep learning based methods for super-resolution have become state-of-the-art and outperform traditional approaches by a significant margin. From the initial models designed for fixed integer scaling factors (e.g. \times 2 or \times 4)), efforts were made to explore different directions such as modeling blur kernels or addressing non-integer scaling factors. However, existing works do not prov...Show More
Generic Image Restoration with Flow Based Priors
Leonhard Helminger;Michael Bernasconi;Abdelaziz Djelouah;Markus Gross;Christopher Schroers
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision with suitable image pairs to tackle a specific degradation. However, in a generic setting with unknown degradations this is not possible and a good prior remains crucial. Recently, neural network based approaches have been ...Show More
Style Transfer for Keypoint Matching Under Adverse Conditions
2020 International Conference on 3D Vision (3DV)
Year: 2020 | Conference Paper |
Cited by: Papers (3)
In this work, we address the difficulty of matching local features between images captured at distant points in time resulting in a global appearance change. Inspired by recent neural style transfer techniques, we propose to use an image transformation network to translate night images into day-like appearance, with the objective of better matching performance. We extend traditional style transfer...Show More
Neural Inter-Frame Compression for Video Coding
Abdelaziz Djelouah;Joaquim Campos;Simone Schaub-Meyer;Christopher Schroers
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (108)
While there are many deep learning based approaches for single image compression, the field of end-to-end learned video coding has remained much less explored. Therefore, in this work we present an inter-frame compression approach for neural video coding that can seamlessly build up on different existing neural image codecs. Our end-to-end solution performs temporal prediction by optical flow base...Show More
PhaseNet for Video Frame Interpolation
Simone Meyer;Abdelaziz Djelouah;Brian McWilliams;Alexander Sorkine-Hornung;Markus Gross;Christopher Schroers
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (132)
Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios with e.g. lighting changes or motion blur. Recent deep learning approaches that rely on kernels to represent motion can only alleviate these problems to some extent. In those cases, methods that use a per-pixel phase-ba...Show More
Normalized Cut Loss for Weakly-Supervised CNN Segmentation
Meng Tang;Abdelaziz Djelouah;Federico Perazzi;Yuri Boykov;Christopher Schroers
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (230)
Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supervised approaches generate full masks from partial input (e.g. scribbles or seeds) using standard interactive segmentation methods as preprocessing. But, errors in such masks result in poorer training since standard loss...Show More
Automatic 3D Car Model Alignment for Mixed Image-Based Rendering
Rodrigo Ortiz-Cayon;Abdelaziz Djelouah;Francisco Massa;Mathieu Aubry;George Drettakis
2016 Fourth International Conference on 3D Vision (3DV)
Year: 2016 | Conference Paper |
Image-Based Rendering (IBR) allows good-quality free-viewpoint navigation in urban scenes, but suffers from artifacts on poorly reconstructed objects, e.g., reflective surfaces such as cars. To alleviate this problem, we propose a method that automatically identifies stock 3D models, aligns them in the 3D scene and performs morphing to better capture image contours. We do this by first adapting le...Show More
Cotemporal Multi-View Video Segmentation
Abdelaziz Djelouah;Jean-Sébastien Franco;Edmond Boyer;Patrick Pérez;George Drettakis
2016 Fourth International Conference on 3D Vision (3DV)
Year: 2016 | Conference Paper |
Cited by: Papers (7)
We address the problem of multi-view video segmentation of dynamic scenes in general and outdoor environments with possibly moving cameras. Multi-view methods for dynamic scenes usually rely on geometric calibration to impose spatial shape constraints between viewpoints. In this paper, we show that the calibration constraint can be relaxed while still getting competitive segmentation results using...Show More
A Bayesian Approach for Selective Image-Based Rendering Using Superpixels
2015 International Conference on 3D Vision
Year: 2015 | Conference Paper |
Image-Based Rendering (IBR) algorithms generate high quality photo-realistic imagery without the burden of detailed modeling and expensive realistic rendering. Recent methods have different strengths and weaknesses, depending on 3D reconstruction quality and scene content. Each algorithm operates with a set of hypotheses about the scene and the novel views, resulting in different quality/speed tra...Show More
Sparse Multi-View Consistency for Object Segmentation
Abdelaziz Djelouah;Jean-Sébastien Franco;Edmond Boyer;François Le Clerc;Patrick Pérez
Multiple view segmentation consists in segmenting objects simultaneously in several views. A key issue in that respect and compared to monocular settings is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is suff...Show More
Multi-view Object Segmentation in Space and Time
Abdelaziz Djelouah;Jean-Sébastien Franco;Edmond Boyer;François Le Clerc;Patrick Pérez
2013 IEEE International Conference on Computer Vision
Year: 2013 | Conference Paper |
In this paper, we address the problem of object segmentation in multiple views or videos when two or more viewpoints of the same scene are available. We propose a new approach that propagates segmentation coherence information in both space and time, hence allowing evidences in one image to be shared over the complete set. To this aim the segmentation is cast as a single efficient labeling problem...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.