Search Results for author: Federico Perazzi

Found 25 papers, 12 papers with code

HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars

no code implementations28 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.

Image Super-Resolution

Content-Aware GAN Compression

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.

Image Generation Image Manipulation +1

Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments

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.

Denoising

Deep Image Compositing

no code implementations4 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.

Image Matting

Shape Adaptor: A Learnable Resizing Module

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.

Image Classification Neural Architecture Search +1

Real-time Semantic Segmentation with Fast Attention

1 code implementation7 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.

Real-Time Semantic Segmentation Segmentation

Scaling Object Detection by Transferring Classification Weights

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.

Classification General Classification +3

Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks

1 code implementation30 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.

Deblurring Image Deblurring

The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

no code implementations2 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).

Object Segmentation +3

Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes

no code implementations25 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.

Depth Estimation Depth Prediction

Normalized Cut Loss for Weakly-supervised CNN Segmentation

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.

Interactive Segmentation Segmentation +1

On Regularized Losses for Weakly-supervised CNN Segmentation

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.

Segmentation Semantic Segmentation

The 2018 DAVIS Challenge on Video Object Segmentation

no code implementations1 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).

Interactive Segmentation Object +4

The 2017 DAVIS Challenge on Video Object Segmentation

no code implementations3 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.

Object Scene Classification +5

Learning Video Object Segmentation from Static Images

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.

Instance Segmentation Object +5

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

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.

Segmentation Semantic Segmentation +3

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