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Benefiting from Multitask Learning to Improve Single Image Super-Resolution

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Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Sciences et Techniques de l’Ingénieur (STI)2017

Webly Supervised Semantic Segmentation

Sabine Süsstrunk, Bin Jin

We propose a weakly supervised semantic segmentation algorithm that uses image tags for supervision. We apply the tags in queries to collect three sets of web images, which encode the clean foregrounds, the common back- grounds, and realistic scenes of the ...

Ieee2017

Learning Aerial Image Segmentation from Online Maps

Martin Jaggi, Jan Dirk Wegner, Aurélien Lucchi, Thomas Hofmann

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

Michaël Unser, Kyong Hwan Jin, Michael Thompson McCann

In this article, we review recent uses of convolutional neural networks (CNNs) to solve inverse problems in imaging. It has recently become feasible to train deep CNNs on large databases of images, and they have shown outstanding performance on object clas ...

IEEE2017

InnerView: Learning Place Ambiance from Social Media Images

Daniel Gatica-Perez, Darshan Santani, Rui Hu

In the recent past, there has been interest in characterizing the physical and social ambiance of urban spaces to understand how people perceive and form impressions of these environments based on physical and psychological constructs. Building on our earl ...

ACM2016

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

Alexandre Massoud Alahi

We consider image transformation problems, where an input image is transformed into an output image. Recent methods for such problems typically train feed-forward convolutional neural networks using a per-pixel loss between the output and ground-truth imag ...

Springer2016

Image Inpainting through Neural Networks Hallucinations

Pascal Frossard, Alhussein Fawzi

We consider in this paper the problem of image inpainting, where the objective is to reconstruct large continuous regions of missing or deteriorated parts of an image. Traditional inpainting algorithms are unfortunately not well adapted to handle such corr ...

Ieee2016

Learning to Refine Object Segments

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Object segmentation requires both object-level information and low-level pixel data. This presents a challenge for feedforward networks: lower layers in convolutional nets capture rich spatial information, while upper layers encode object-level knowledge b ...

Springer2016

From Image-level to Pixel-level Labeling with Convolutional Networks

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and natur ...

2015

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

We study the problem of perceiving forest or mountain trails from a single monocular image acquired from the viewpoint of a robot traveling on the trail itself. Previous literature focused on trail segmentation, and used low-level features such as image sa ...

2015