The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous proposons un cadre d'apprentissage incrémental en classe pour la reconnaissance de l'activité humaine basé sur le modèle Bag-of-Sequencelets (BoS). Le framework met à jour efficacement les modèles appris sans avoir à les réapprendre lorsque les données d'entraînement de nouvelles classes sont ajoutées. Dans ce cadre, tous les types de fonctionnalités, y compris les fonctionnalités créées à la main et les fonctionnalités basées sur les réseaux de neurones convolutifs (CNN) et les combinaisons de ces fonctionnalités, peuvent être utilisées comme fonctionnalités pour les vidéos. Par rapport au BoS d'origine, le nouveau cadre peut réduire considérablement le temps d'apprentissage avec peu de perte de précision de la classification.
Jong-Woo LEE
Pohang University of Science and Technology (POSTECH)
Ki-Sang HONG
Pohang University of Science and Technology (POSTECH)
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Jong-Woo LEE, Ki-Sang HONG, "Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 9, pp. 1293-1302, September 2019, doi: 10.1587/transfun.E102.A.1293.
Abstract: We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1293/_p
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@ARTICLE{e102-a_9_1293,
author={Jong-Woo LEE, Ki-Sang HONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition},
year={2019},
volume={E102-A},
number={9},
pages={1293-1302},
abstract={We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.},
keywords={},
doi={10.1587/transfun.E102.A.1293},
ISSN={1745-1337},
month={September},}
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TY - JOUR
TI - Efficient Class-Incremental Learning Based on Bag-of-Sequencelets Model for Activity Recognition
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1293
EP - 1302
AU - Jong-Woo LEE
AU - Ki-Sang HONG
PY - 2019
DO - 10.1587/transfun.E102.A.1293
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2019
AB - We propose a class-incremental learning framework for human activity recognition based on the Bag-of-Sequencelets model (BoS). The framework updates learned models efficiently without having to relearn them when training data of new classes are added. In this framework, all types of features including hand-crafted features and Convolutional Neural Networks (CNNs) based features and combinations of those features can be used as features for videos. Compared with the original BoS, the new framework can reduce the learning time greatly with little loss of classification accuracy.
ER -