ETH Zurich

∙ Zürich , Switzerland ∙ 0 followers

ETH Zurich is a science, technology, engineering and mathematics university in the city of Zürich, Switzerland.

Featured Researchers

Yang Liu
213 publications
Luc Van Gool
158 publications
Andreas Krause
80 publications
Roland Siegwart
72 publications
Luca Benini
67 publications
Onur Mutlu
67 publications
Yue Zhang
66 publications
Ce Zhang
49 publications
Radu Timofte
48 publications
Martin Jaggi
47 publications
Marc Pollefeys
46 publications

Ghost Units Yield Biologically Plausible Backprop in Deep Neural Networks

In the past few years, deep learning has transformed artificial intellig...
11/15/2019 ∙ by Thomas Mesnard, et al. ∙ 37 ∙ share

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Fine-grained Recognition: Accounting for Subtle Differences between Similar Classes

The main requisite for fine-grained recognition task is to focus on subt...
12/14/2019 ∙ by Guolei Sun, et al. ∙ 37 ∙ share

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(1 + ε)-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

Anomaly detection is not an easy problem since distribution of anomalous...
06/14/2019 ∙ by Maxim Borisyak, et al. ∙ 33 ∙ share

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No-Regret Learning in Unknown Games with Correlated Payoffs

We consider the problem of learning to play a repeated multi-agent game ...
09/18/2019 ∙ by Pier Giuseppe Sessa, et al. ∙ 33 ∙ share

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RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials

In this paper, we consider the problem of reconstructing a dense 3D mode...
01/06/2019 ∙ by Despoina Paschalidou, et al. ∙ 28 ∙ share

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An Anatomy of Graph Neural Networks Going Deep via the Lens of Mutual Information: Exponential Decay vs. Full Preservation

Graph Convolutional Network (GCN) has attracted intensive interests rece...
10/10/2019 ∙ by Nezihe Merve Gürel, et al. ∙ 28 ∙ share

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No-regret Bayesian Optimization with Unknown Hyperparameters

Bayesian optimization (BO) based on Gaussian process models is a powerfu...
01/10/2019 ∙ by Felix Berkenkamp, et al. ∙ 26 ∙ share

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Active Learning for Segmentation Based on Bayesian Sample Queries

Segmentation of anatomical structures is a fundamental image analysis ta...
12/22/2019 ∙ by Firat Ozdemir, et al. ∙ 26 ∙ share

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Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions

Evolutionary Strategies (ES) are known to be an effective black-box opti...
10/11/2019 ∙ by Florian Meier, et al. ∙ 24 ∙ share

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EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

We present the first event-based learning approach for motion segmentati...
03/18/2019 ∙ by Anton Mitrokhin, et al. ∙ 22 ∙ share

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This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity

There has been a remarkable progress in the accuracy of semantic segment...
09/02/2019 ∙ by David Haldimann, et al. ∙ 20 ∙ share

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Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution

In single image super-resolution (SISR), given a low-resolution (LR) ima...
10/09/2019 ∙ by Xin Deng, et al. ∙ 20 ∙ share

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Efficient Video Semantic Segmentation with Labels Propagation and Refinement

This paper tackles the problem of real-time semantic segmentation of hig...
12/26/2019 ∙ by Matthieu Paul, et al. ∙ 20 ∙ share

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Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene

This work addresses the task of dense 3D reconstruction of a complex dyn...
11/19/2019 ∙ by Suryansh Kumar, et al. ∙ 19 ∙ share

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Information-Directed Exploration for Deep Reinforcement Learning

Efficient exploration remains a major challenge for reinforcement learni...
12/18/2018 ∙ by Nikolay Nikolov, et al. ∙ 18 ∙ share

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Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

The goal of many computer vision systems is to transform image pixels in...
01/06/2020 ∙ by Nadine Rueegg, et al. ∙ 18 ∙ share

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GLAMpoints: Greedily Learned Accurate Match points

We introduce a novel CNN-based feature point detector - GLAMpoints - lea...
08/19/2019 ∙ by Prune Truong, et al. ∙ 16 ∙ share

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Meta Answering for Machine Reading

We investigate a framework for machine reading, inspired by real world i...
11/11/2019 ∙ by Benjamin Borschinger, et al. ∙ 16 ∙ share

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AI Benchmark: Running Deep Neural Networks on Android Smartphones

Over the last years, the computational power of mobile devices such as s...
10/02/2018 ∙ by Andrey Ignatov, et al. ∙ 14 ∙ share

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Interventional Robustness of Deep Latent Variable Models

The ability to learn disentangled representations that split underlying ...
10/31/2018 ∙ by Raphael Suter, et al. ∙ 14 ∙ share

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SMIT: Stochastic Multi-Label Image-to-Image Translation

Cross-domain mapping has been a very active topic in recent years. Given...
12/10/2018 ∙ by Andrés Romero, et al. ∙ 14 ∙ share

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An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms

This paper focuses on valuating training data for supervised learning ta...
11/17/2019 ∙ by Ruoxi Jia, et al. ∙ 14 ∙ share

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Self-supervised Object Motion and Depth Estimation from Video

We present a self-supervised learning framework to estimate the individu...
12/09/2019 ∙ by Qi Dai, et al. ∙ 14 ∙ share

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Knowledge-Induced Learning with Adaptive Sampling Variational Autoencoders for Open Set Fault Diagnostics

The recent increase in the availability of system condition monitoring d...
12/28/2019 ∙ by Manuel Arias Chao, et al. ∙ 14 ∙ share

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Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

Recent advances in generative models and adversarial training have led t...
09/18/2019 ∙ by Samarth Shukla, et al. ∙ 13 ∙ share

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DLOW: Domain Flow for Adaptation and Generalization

In this work, we propose a domain flow generation(DLOW) approach to mode...
12/13/2018 ∙ by Rui Gong, et al. ∙ 12 ∙ share

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AReS and MaRS - Adversarial and MMD-Minimizing Regression for SDEs

Stochastic differential equations are an important modeling class in man...
02/22/2019 ∙ by Gabriele Abbati, et al. ∙ 12 ∙ share

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Adversarial Training Generalizes Data-dependent Spectral Norm Regularization

We establish a theoretical link between adversarial training and operato...
06/04/2019 ∙ by Kevin Roth, et al. ∙ 12 ∙ share

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Learning Filter Basis for Convolutional Neural Network Compression

Convolutional neural networks (CNNs) based solutions have achieved state...
08/23/2019 ∙ by Yawei Li, et al. ∙ 12 ∙ share

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AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results

This paper reviews the AIM 2019 challenge on real world super-resolution...
11/18/2019 ∙ by Andreas Lugmayr, et al. ∙ 12 ∙ share

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EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators

In the wake of the success of convolutional neural networks in image cla...
08/30/2019 ∙ by Lukas Cavigelli, et al. ∙ 11 ∙ share

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HistoNet: Predicting size histograms of object instances

We propose to predict histograms of object sizes in crowded scenes direc...
12/11/2019 ∙ by Kishan Sharma, et al. ∙ 11 ∙ share

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XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference

Binary Neural Networks (BNNs) are promising to deliver accuracy comparab...
07/09/2018 ∙ by Francesco Conti, et al. ∙ 10 ∙ share

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Real-time 3D Traffic Cone Detection for Autonomous Driving

Considerable progress has been made in semantic scene understanding of r...
02/06/2019 ∙ by Ankit Dhall, et al. ∙ 10 ∙ share

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ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

Parameter inference in ordinary differential equations is an important p...
02/17/2019 ∙ by Philippe Wenk, et al. ∙ 10 ∙ share

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Learning Content-Weighted Deep Image Compression

Learning-based lossy image compression usually involves the joint optimi...
04/01/2019 ∙ by Mu Li, et al. ∙ 10 ∙ share

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An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments

The ability to plan informative paths online is essential to robot auton...
09/20/2019 ∙ by Lukas Schmid, et al. ∙ 10 ∙ share

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Oracle inequalities for image denoising with total variation regularization

We derive oracle results for discrete image denoising with a total varia...
11/17/2019 ∙ by Francesco Ortelli, et al. ∙ 10 ∙ share

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Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Nowadays, the increasingly growing number of mobile and computing device...
12/15/2019 ∙ by Suman Saha, et al. ∙ 10 ∙ share

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3D Appearance Super-Resolution with Deep Learning

We tackle the problem of retrieving high-resolution (HR) texture maps of...
06/03/2019 ∙ by Yawei Li, et al. ∙ 9 ∙ share

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Target-less registration of point clouds: A review

Point cloud registration has been one of the basic steps of point cloud ...
12/29/2019 ∙ by Yue Pan, et al. ∙ 9 ∙ share

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MTI-Net: Multi-Scale Task Interaction Networks for Multi-Task Learning

In this paper, we highlight the importance of considering task interacti...
01/19/2020 ∙ by Simon Vandenhende, et al. ∙ 9 ∙ share

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Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach

Recently, increasing attention has been drawn to training semantic segme...
12/12/2018 ∙ by Yuhua Chen, et al. ∙ 8 ∙ share

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Neural Sequential Phrase Grounding (SeqGROUND)

We propose an end-to-end approach for phrase grounding in images. Unlike...
03/18/2019 ∙ by Pelin Dogan, et al. ∙ 8 ∙ share

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PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

This paper introduces a newly collected and novel dataset (StereoMSI) fo...
04/01/2019 ∙ by Mehrdad Shoeiby, et al. ∙ 8 ∙ share

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Simultaneous multi-view instance detection with learned geometric soft-constraints

We propose to jointly learn multi-view geometry and warping between view...
07/25/2019 ∙ by Ahmed Samy Nassar, et al. ∙ 8 ∙ share

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Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences

Predicting the remaining useful life of machinery, infrastructure, or ot...
07/11/2019 ∙ by Mathias Kraus, et al. ∙ 7 ∙ share

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Semantic Understanding of Foggy Scenes with Purely Synthetic Data

This work addresses the problem of semantic scene understanding under fo...
10/09/2019 ∙ by Martin Hahner, et al. ∙ 7 ∙ share

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Analysis of the generalization error: Empirical risk minimization over deep artificial neural networks overcomes the curse of dimensionality in the numerical approximation of B

The development of new classification and regression algorithms based on...
09/09/2018 ∙ by Julius Berner, et al. ∙ 6 ∙ share

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Practical Full Resolution Learned Lossless Image Compression

We propose the first practical learned lossless image compression system...
11/30/2018 ∙ by Fabian Mentzer, et al. ∙ 6 ∙ share

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