no code implementations • 28 Mar 2022 • Xiaoyu Xiang, Jon Morton, Fitsum A Reda, Lucas Young, Federico Perazzi, Rakesh Ranjan, Amit Kumar, Andrea Colaco, Jan Allebach
Compared with previous methods, our network can effectively handle the misalignment between the input and the reference without requiring facial priors and learn the aggregated reference set representation in an end-to-end manner.
1 code implementation • 3 Jun 2021 • Juan Leon Alcazar, Long Mai, Federico Perazzi, Joon-Young Lee, Pablo Arbelaez, Bernard Ghanem, Fabian Caba Heilbron
To showcase the potential of our new dataset, we propose an audiovisual baseline and benchmark for person retrieval.
1 code implementation • CVPR 2021 • Yuchen Liu, Zhixin Shu, Yijun Li, Zhe Lin, Federico Perazzi, S. Y. Kung
We then propose a novel content-aware method to guide the processes of both pruning and distillation.
no code implementations • CVPR 2021 • Zhihao Xia, Michaël Gharbi, Federico Perazzi, Kalyan Sunkavalli, Ayan Chakrabarti
We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments.
no code implementations • 4 Nov 2020 • He Zhang, Jianming Zhang, Federico Perazzi, Zhe Lin, Vishal M. Patel
In this paper, we propose a new method which can automatically generate high-quality image compositing without any user input.
1 code implementation • ECCV 2020 • Shikun Liu, Zhe Lin, Yilin Wang, Jianming Zhang, Federico Perazzi, Edward Johns
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution.
1 code implementation • ECCV 2020 • Rui Zhu, Xingyi Yang, Yannick Hold-Geoffroy, Federico Perazzi, Jonathan Eisenmann, Kalyan Sunkavalli, Manmohan Chandraker
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity.
1 code implementation • 7 Jul 2020 • Ping Hu, Federico Perazzi, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Kate Saenko, Stan Sclaroff
The proposed architecture relies on our fast spatial attention, which is a simple yet efficient modification of the popular self-attention mechanism and captures the same rich spatial context at a small fraction of the computational cost, by changing the order of operations.
Ranked #32 on
Semantic Segmentation
on DensePASS
1 code implementation • CVPR 2020 • Juan Leon Alcazar, Fabian Caba Heilbron, Long Mai, Federico Perazzi, Joon-Young Lee, Pablo Arbelaez, Bernard Ghanem
Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker.
Active Speaker Detection
Audio-Visual Active Speaker Detection
1 code implementation • CVPR 2020 • Ping Hu, Fabian Caba Heilbron, Oliver Wang, Zhe Lin, Stan Sclaroff, Federico Perazzi
We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation.
Ranked #2 on
Video Semantic Segmentation
on Cityscapes val
no code implementations • ICCV 2019 • Sai Bi, Kalyan Sunkavalli, Federico Perazzi, Eli Shechtman, Vladimir Kim, Ravi Ramamoorthi
We present a method to improve the visual realism of low-quality, synthetic images, e. g. OpenGL renderings.
no code implementations • CVPR 2020 • Zhihao Xia, Federico Perazzi, Michaël Gharbi, Kalyan Sunkavalli, Ayan Chakrabarti
Bursts of images exhibit significant self-similarity across both time and space.
1 code implementation • ICCV 2019 • Jason Kuen, Federico Perazzi, Zhe Lin, Jianming Zhang, Yap-Peng Tan
Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count.
1 code implementation • 30 Jul 2019 • Rajeev Yasarla, Federico Perazzi, Vishal M. Patel
We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring.
no code implementations • 2 May 2019 • Sergi Caelles, Jordi Pont-Tuset, Federico Perazzi, Alberto Montes, Kevis-Kokitsi Maninis, Luc van Gool
We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmentation (VOS).
no code implementations • 25 Apr 2019 • Chaoyang Wang, Simon Lucey, Federico Perazzi, Oliver Wang
We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e. g., people.
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 #20 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 • 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.
no code implementations • 1 Mar 2018 • Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis, Yu-Hua Chen, Luc van Gool, Federico Perazzi, Jordi Pont-Tuset
Motivated by the analysis of the results of the 2017 edition, the main track of the competition will be the same than in the previous edition (segmentation given the full mask of the objects in the first frame -- semi-supervised scenario).
no code implementations • 3 Apr 2017 • Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine-Hornung, Luc van Gool
The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques.
2 code implementations • CVPR 2017 • Anna Khoreva, Federico Perazzi, Rodrigo Benenson, Bernt Schiele, Alexander Sorkine-Hornung
Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation.
Ranked #6 on
Semi-Supervised Video Object Segmentation
on YouTube
1 code implementation • CVPR 2016 • Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc van Gool, Markus Gross, Alexander Sorkine-Hornung
The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motion-blur and appearance changes.
no code implementations • CVPR 2016 • Nicolas Maerki, Federico Perazzi, Oliver Wang, Alexander Sorkine-Hornung
In this work, we propose a novel approach to video segmentation that operates in bilateral space.
Ranked #76 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
no code implementations • ICCV 2015 • Federico Perazzi, Oliver Wang, Markus Gross, Alexander Sorkine-Hornung
We present a novel approach to video segmentation using multiple object proposals.
Ranked #77 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016