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20162021
most citedWebVision Database: Visual Learning and Understanding from Web Data

313 citations · 337 across the 3 of their papers we have counts for

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7 papers · 1 filter

cs.CV2018

Interactive Full Image Segmentation by Considering All Regions Jointly

Eirikur Agustsson, Jasper R. R. Uijlings, Vittorio Ferrari

We address interactive full image annotation, where the goal is to accurately segment all object and stuff regions in an image. We propose an interactive, scribble-based annotation…

cs.CV2018

PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Andrey Ignatov, Radu Timofte, Thang Van Vu +45

This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…

cs.CV2018

Generative Adversarial Networks for Extreme Learned Image Compression

Eirikur Agustsson, Michael Tschannen, Fabian Mentzer +2

We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale di…

cs.CV2018

Towards Image Understanding from Deep Compression without Decoding

Robert Torfason, Fabian Mentzer, Eirikur Agustsson +3

Motivated by recent work on deep neural network (DNN)-based image compression methods showing potential improvements in image quality, savings in storage, and bandwidth reduction,…

cs.CV2017

ComboGAN: Unrestrained Scalability for Image Domain Translation

Asha Anoosheh, Eirikur Agustsson, Radu Timofte +1

This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two…

cs.CV2017313 cited

WebVision Database: Visual Learning and Understanding from Web Data

Wen Li, Limin Wang, Wei Li +2

In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than mi…