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Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Publications associées (36)

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Deep Learning‐based Semantic Segmentation in Remote Sensing

Devis Tuia

Semantic segmentation consists of the generation of a categorical map, given an image in which each pixel of the image is automatically assigned a class. Deep learning allows the influence of the pixel's context to be learned by capturing the non-linear re ...

Wiley2021

Enabling autonomous driving (AD) can be considered one of the biggest challenges in today?s technology. AD is a complex task accomplished by several functionalities, with environment perception being one of its core functions. Environment perception is usu ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Datasets and Models for Historical Newspaper Article Segmentation

Maud Ehrmann, Sofia Ares Oliveira, Raphaël Barman

Dataset and models used and produced in the work described in the paper "Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers": https://infoscience.epfl.ch/record/282863?ln=en ...

2021

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Humor recognition has been widely studied as a text classification problem using data-driven approaches. However, most existing work does not examine the actual joke mechanism to understand humor. We break down any joke into two distinct components: the se ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Vision based pixel-level bridge structural damage detection using a link ASPP network

Wenlong Deng

Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface s ...

2020

Neural networks for semantic segmentation of historical city maps: Cross-cultural performance and the impact of figurative diversity

Rémi Guillaume Petitpierre

In this work, we present a new semantic segmentation model for historical city maps that surpasses the state of the art in terms of flexibility and performance. Research in automatic map processing is largely focused on homogeneous corpora or even individu ...

2020

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

EPFL2019

Theoretical foundations of forward feature selection methods based on mutual information

Francisco Santos Paredes Quartin de Macedo

Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic. Among the various ...

ELSEVIER SCIENCE BV2019

EMPIRICAL EVALUATION AND COMBINATION OF PUNCTUATION PREDICTION MODELS APPLIED TO BROADCAST NEWS

Philip Neil Garner

Natural language processing techniques are dependent upon punctuation to work well. When their input is taken from speech recognition, it is necessary to reconstruct the punctuation; in particular sentence boundaries. We define a range of features from low ...

2019

EMPIRICAL EVALUATION AND COMBINATION OF PUNCTUATION PREDICTION MODELS APPLIED TO BROADCAST NEWS

Philip Neil Garner

Idiap2019