Publications related to Estimating Image Depth in the Comics Domain

Related publications (16)

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Monitoring the health of complex industrial assets is crucial for safe and efficient operations. Health indicators that provide quantitative real-time insights into the health status of industrial assets over time serve as valuable tools for, e.g., fault d ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Amir Roshan Zamir, Shuqing Teresa Yeo, Oguzhan Fatih Kar

We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback sig ...

Ieee Computer Soc2023

On unifying sparsity and geometry for image-based 3D scene representation

Ivana Tosic

Demand has emerged for next generation visual technologies that go beyond conventional 2D imaging. Such technologies should capture and communicate all perceptually relevant three-dimensional information about an environment to a distant observer, providin ...

EPFL2009

Complex-Valued-Autoencoder for Structural Health Monitoring with Frequency Modulated Continuous Wave Radar

Olga Fink, Gaëtan Michel Frusque

The occurrence of manufacturing defects in wind turbine blade (WTB) production can result in significant increases in operation and maintenance costs of WTBs, reduce capacity factors of wind farms, and occasionally lead to severe and disastrous consequence ...

Research Publishing2023

Masked Training of Neural Networks with Partial Gradients

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

State-of-the-art training algorithms for deep learning models are based on stochastic gradient descent (SGD). Recently, many variations have been explored: perturbing parameters for better accuracy (such as in Extra-gradient), limiting SGD updates to a sub ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Image/video representation and scalable coding using redundant dictionaries

Adel Rahmoune

Compact or efficient representation for either images or image sequences is key operation to performing image and video processing tasks, such as compression, analysis, etc. The efficiency of an approximation is evaluated by the sparsity measure of the app ...

EPFL2005

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Pascal Fua, Mathieu Salzmann, Kwang Moo Yi, Yinlin Hu, Zheng Dang

Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square problem, which can be done by finding the eigenvector corresponding to the smal ...

IEEE COMPUTER SOC2021

Comparing CTC and LFMMI for out-of-domain adaptation of wav2vec 2.0 acoustic model

Hervé Bourlard, Apoorv Vyas

In this work, we investigate if the wav2vec 2.0 self-supervised pretraining helps mitigate the overfitting issues with connectionist temporal classification (CTC) training to reduce its performance gap with flat-start lattice-free MMI (E2E-LFMMI) for autom ...

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Investigating the cracking of plastered stone masonry walls under shear–compression loading

Katrin Beyer, Michele Godio, Amir Rezaie

Cracks are the most important source of information about the damage that occurs to unreinforced masonry piers under seismic actions. To predict the structural state of unreinforced masonry piers after an earthquake, research models have been developed to ...

2021

Upgrading the Newsroom: An Automated Image Selection System for News Articles

Karl Aberer, Rémi Philippe Lebret

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level wo ...

ASSOC COMPUTING MACHINERY2020

Deep-learning projector for optical diffraction tomography

Michaël Unser, Thanh-An Michel Pham, Harshit Gupta

Optical diffraction tomography is an effective tool to estimate the refractive indices of unknown objects. It proceeds by solving an ill-posed inverse problem for which the wave equation governs the scattering events. The solution has traditionally been de ...

2020

Multi-task additive models with shared transfer functions based on dictionary learning

Pascal Frossard, Alhussein Fawzi

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

Institute of Electrical and Electronics Engineers2017

Saliency-based representations and multi-component classifiers for visual scene recognition

Marco Fornoni

Visual scene recognition deals with the problem of automatically recognizing the high-level semantic concept describing a given image as a whole, such as the environment in which the scene is occurring (e.g. a mountain), or the event that is taking place ( ...

EPFL2014

Improved Local Binary Pattern Based Action Unit Detection Using Morphological and Bilateral Filters

Jean-Philippe Thiran, Matteo Sorci, Anil Yuce

Automatic facial action unit (AU) detection in videos is the key ingredient to all systems that utilize a subject face for either interaction or analysis purposes. With the ever growing range of possible applications, achieving a high accuracy in the simpl ...

2013

Geometric Variational Models for Inverse Problems in Imaging

Virginia Estellers Casas

This dissertation develops geometric variational models for different inverse problems in imaging that are ill-posed, designing at the same time efficient numerical algorithms to compute their solutions. Variational methods solve inverse problems by the fo ...

EPFL2013

Distributed Compressed Representation of Correlated Image Sets

Vijayaraghavan Thirumalai

Vision sensor networks and video cameras find widespread usage in several applications that rely on effective representation of scenes or analysis of 3D information. These systems usually acquire multiple images of the same 3D scene from different viewpoin ...

EPFL2012