1 code implementation • 7 Mar 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • 1 Dec 2021 • Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan
We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.
no code implementations • 25 Nov 2021 • Suhani Vora, Noha Radwan, Klaus Greff, Henning Meyer, Kyle Genova, Mehdi S. M. Sajjadi, Etienne Pot, Andrea Tagliasacchi, Daniel Duckworth
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone.
no code implementations • 25 Nov 2021 • Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi
In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.
1 code implementation • CVPR 2021 • Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.
3 code implementations • ICLR 2020 • Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Schölkopf
Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.
no code implementations • ECCV 2018 • Tae Hyun Kim, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Scholkopf
State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information.
no code implementations • 20 Jul 2018 • Eduardo Pérez-Pellitero, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf
Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences.
4 code implementations • NeurIPS 2018 • Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.
no code implementations • ICML 2018 • Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf
A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.
no code implementations • CVPR 2018 • Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown
Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.
Ranked #5 on
Video Super-Resolution
on Vid4 - 4x upscaling
3 code implementations • ICCV 2017 • Mehdi S. M. Sajjadi, Bernhard Schölkopf, Michael Hirsch
Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input.
no code implementations • 6 Sep 2016 • Mehdi S. M. Sajjadi, Rolf Köhler, Bernhard Schölkopf, Michael Hirsch
Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks.
no code implementations • 2 Jun 2015 • Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike Von Luxburg
Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs).