Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Kazuhiko Kawamoto"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correla
Externí odkaz:
https://doaj.org/article/ce066ca5df2c447ea300670d277606fe
Publikováno v:
IEEE Access, Vol 10, Pp 59534-59543 (2022)
Unsupervised domain adaptation, which involves transferring knowledge from a label-rich source domain to an unlabeled target domain, can be used to reduce annotation costs in the field of object detection substantially. This study demonstrates that a
Externí odkaz:
https://doaj.org/article/5a2f759e7afa459a81eac06c15e15af0
Autor:
Kazuma Kurisaki, Kazuhiko Kawamoto
Publikováno v:
IEEE Access, Vol 9, Pp 3269-3277 (2021)
We propose a method for animating static images using a generative adversarial network (GAN). Given a source image depicting a cloud image and a driving video sequence depicting a moving cloud image, our framework generates a video in which the sourc
Externí odkaz:
https://doaj.org/article/24d77b261b9445e7837e55fcc1506285
Autor:
Kodai Uchiyama, Kazuhiko Kawamoto
Publikováno v:
IEEE Access, Vol 9, Pp 50106-50111 (2021)
We present an audio-visual model for generating food texture sounds from silent eating videos. We designed a deep network-based model that takes the visual features of the detected faces as input and outputs a magnitude spectrogram that aligns with t
Externí odkaz:
https://doaj.org/article/799d96852ff649f4b762e772e76d499e
Publikováno v:
Sensors, Vol 23, Iss 5, p 2515 (2023)
In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos. Building sequential variational autoen
Externí odkaz:
https://doaj.org/article/e6f5521f78274178bdf9bcf883dce619
Autor:
Kazuma Fujii, Kazuhiko Kawamoto
Publikováno v:
Array, Vol 11, Iss , Pp 100071- (2021)
Unsupervised cross-domain object detection has recently attracted considerable attention because of its ability to significantly reduce annotation costs. For two-stage detectors, several improvements have been made in feature-level adaptations. Howev
Externí odkaz:
https://doaj.org/article/c9c22803a879421cbaac7f9d30e435f8
Publikováno v:
EURASIP Journal on Image and Video Processing, Vol 2018, Iss 1, Pp 1-13 (2018)
Abstract We propose a vision-based method for recognizing first-person reading activity with deep learning. For the success of deep learning, it is well known that a large amount of training data plays a vital role. Unlike image classification, there
Externí odkaz:
https://doaj.org/article/153ea353ecf642d6806745e5d1a77558
Autor:
Nan Wu, Kazuhiko Kawamoto
Publikováno v:
Sensors, Vol 21, Iss 11, p 3793 (2021)
Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive. This has encouraged research in zero-shot action recogniti
Externí odkaz:
https://doaj.org/article/e3db3f84e430486e867658553bc121ac
Publikováno v:
Sensors, Vol 20, Iss 15, p 4195 (2020)
Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurre
Externí odkaz:
https://doaj.org/article/6cdc8f50e13243bfa82e819d63517863
Publikováno v:
Applied Intelligence (Dordrecht, Netherlands)
In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sund
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::657c7716b9adc0a66ce2d505d6511213
http://europepmc.org/articles/PMC8731199
http://europepmc.org/articles/PMC8731199