Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Youngjung Uh"'
Publikováno v:
npj Precision Oncology, Vol 7, Iss 1, Pp 1-9 (2023)
Abstract Accurate identification of molecular alterations in gliomas is crucial for their diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) allows for the observation of diverse and heterogeneous alterations, it is inherent
Externí odkaz:
https://doaj.org/article/02be410b27fc438c8645c018526fb457
Publikováno v:
Neurocomputing. 445:244-254
Weakly supervised learning attempts to construct predictive models by learning with weak supervision. In this paper, we concentrate on weakly supervised object localization and semantic segmentation tasks. Existing methods are limited to focusing on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4247cb88554cddd227208fe0712e683c
https://doi.org/10.1016/j.neucom.2021.03.023
https://doi.org/10.1016/j.neucom.2021.03.023
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031197802
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2a0c81bc93716dfebef2b693cc1c5be
https://doi.org/10.1007/978-3-031-19781-9_40
https://doi.org/10.1007/978-3-031-19781-9_40
Publikováno v:
2021 IEEE/CVF International Conference on Computer Vision (ICCV).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4b3065d9050bf18c092d6884d2af499
https://doi.org/10.1109/iccv48922.2021.00280
https://doi.org/10.1109/iccv48922.2021.00280
Publikováno v:
Neurocomputing. 351:187-195
Visual question answering (VQA) aims reasoning answers given a pair of textual question and image. Previous approaches for VQA use only the highest layer of a Convolutional Neural Network (CNN) for visual representation, which biases on object classi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a217a801365e19f76814e6641a2150d6
https://doi.org/10.1016/j.neucom.2019.03.035
https://doi.org/10.1016/j.neucom.2019.03.035
Publikováno v:
CVPR
Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87308ad19d9954049310151eb3131a63
http://arxiv.org/abs/2104.14754
http://arxiv.org/abs/2104.14754
In generative adversarial networks, improving discriminators is one of the key components for generation performance. As image classifiers are biased toward texture and debiasing improves accuracy, we investigate 1) if the discriminators are biased,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c616e1366077c1cd84380ad6a3c171a
Publikováno v:
Computer Vision – ACCV 2020 ISBN: 9783030695378
ACCV (4)
ACCV (4)
Weakly supervised object localization (WSOL) aims to localize the target object using only the image-level supervision. Recent methods encourage the model to activate feature maps over the entire object by dropping the most discriminative parts. Howe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea31187be0382486d5478960a47c00e2
https://doi.org/10.1007/978-3-030-69538-5_1
https://doi.org/10.1007/978-3-030-69538-5_1
Training GANs on videos is even more sophisticated than on images because videos have a distinguished dimension: time. While recent methods designed a dedicated architecture considering time, generated videos are still far from indistinguishable from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1fc05766a2eb1db82c19291af1956476
Publikováno v:
CVPR
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb977510d5afc5b430dc2168ebb03973
https://doi.org/10.1109/cvpr42600.2020.00821
https://doi.org/10.1109/cvpr42600.2020.00821