253 citations · 619 across the 20 of their papers we have counts for
22 papers
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
Xiaoyu Xiang, Jon Morton, Fitsum A Reda +6
A promising direction for recovering the lost information in low-resolution headshot images is utilizing a set of high-resolution exemplars from the same identity. Complementary im…
APES: Audiovisual Person Search in Untrimmed Video
Juan Leon Alcazar, Long Mai, Federico Perazzi +4
Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the autom…
Content-Aware GAN Compression
Yuchen Liu, Zhixin Shu, Yijun Li +3
Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders th…
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments
Zhihao Xia, Michaël Gharbi, Federico Perazzi +2
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. Our goal is to produce a high-…
Deep Image Compositing
He Zhang, Jianming Zhang, Federico Perazzi +2
Image compositing is a task of combining regions from different images to compose a new image. A common use case is background replacement of portrait images. To obtain high qualit…
Shape Adaptor: A Learnable Resizing Module
Shikun Liu, Zhe Lin, Yilin Wang +3
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 st…
Real-time Semantic Segmentation with Fast Attention
Ping Hu, Federico Perazzi, Fabian Caba Heilbron +4
In deep CNN based models for semantic segmentation, high accuracy relies on rich spatial context (large receptive fields) and fine spatial details (high resolution), both of which…
Single View Metrology in the Wild
Rui Zhu, Xingyi Yang, Yannick Hold-Geoffroy +4
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity. We present a novel approach to single view metrology that can recover the absolute…
Active Speakers in Context
Juan Leon Alcazar, Fabian Caba Heilbron, Long Mai +4
Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. Although this strategy can be enough for addressing single…
Temporally Distributed Networks for Fast Video Semantic Segmentation
Ping Hu, Fabian Caba Heilbron, Oliver Wang +3
We present TDNet, a temporally distributed network designed for fast and accurate video semantic segmentation. We observe that features extracted from a certain high-level layer of…
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
Sai Bi, Kalyan Sunkavalli, Federico Perazzi +3
We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image spac…
Basis Prediction Networks for Effective Burst Denoising with Large Kernels
Zhihao Xia, Federico Perazzi, Michaël Gharbi +2
Bursts of images exhibit significant self-similarity across both time and space. This motivates a representation of the kernels as linear combinations of a small set of basis eleme…
Scaling Object Detection by Transferring Classification Weights
Jason Kuen, Federico Perazzi, Zhe Lin +2
Large scale object detection datasets are constantly increasing their size in terms of the number of classes and annotations count. Yet, the number of object-level categories annot…
Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
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. The proposed Uncertainty Guided Multi- Stream Seman…
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
Sergi Caelles, Jordi Pont-Tuset, Federico Perazzi +3
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 Segmen…
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
Chaoyang Wang, Simon Lucey, Federico Perazzi +1
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. In order to learn recon…
A Fully Progressive Approach to Single-Image Super-Resolution
Yifan Wang, Federico Perazzi, Brian McWilliams +3
Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each ca…
On Regularized Losses for Weakly-supervised CNN Segmentation
Meng Tang, Federico Perazzi, Abdelaziz Djelouah +3
Minimization of regularized losses is a principled approach to weak supervision well-established in deep learning, in general. However, it is largely overlooked in semantic segment…
Normalized Cut Loss for Weakly-supervised CNN Segmentation
Meng Tang, Abdelaziz Djelouah, Federico Perazzi +2
Most recent semantic segmentation methods train deep convolutional neural networks with fully annotated masks requiring pixel-accuracy for good quality training. Common weakly-supe…
The 2018 DAVIS Challenge on Video Object Segmentation
Sergi Caelles, Alberto Montes, Kevis-Kokitsi Maninis +4
We present the 2018 DAVIS Challenge on Video Object Segmentation, a public competition specifically designed for the task of video object segmentation. It builds upon the DAVIS 201…
The 2017 DAVIS Challenge on Video Object Segmentation
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles +3
We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Foll…
Learning Video Object Segmentation from Static Images
Anna Khoreva, Federico Perazzi, Rodrigo Benenson +2
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 segmentati…