Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Jeon, Seogkyu"'
Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any fur
Externí odkaz:
http://arxiv.org/abs/2308.10554
Autor:
Hong, Kibeom, Jeon, Seogkyu, Lee, Junsoo, Ahn, Namhyuk, Kim, Kunhee, Lee, Pilhyeon, Kim, Daesik, Uh, Youngjung, Byun, Hyeran
To deliver the artistic expression of the target style, recent studies exploit the attention mechanism owing to its ability to map the local patches of the style image to the corresponding patches of the content image. However, because of the low sem
Externí odkaz:
http://arxiv.org/abs/2307.09724
This paper focuses on subject adaptation for EEG-based visual recognition. It aims at building a visual stimuli recognition system customized for the target subject whose EEG samples are limited, by transferring knowledge from abundant data of source
Externí odkaz:
http://arxiv.org/abs/2301.08448
Publikováno v:
Expert Systems with Applications 205 (2022): 117697
Domain adaptation for object detection (DAOD) has recently drawn much attention owing to its capability of detecting target objects without any annotations. To tackle the problem, previous works focus on aligning features extracted from partial level
Externí odkaz:
http://arxiv.org/abs/2207.09613
This paper tackles the problem of subject adaptive EEG-based visual recognition. Its goal is to accurately predict the categories of visual stimuli based on EEG signals with only a handful of samples for the target subject during training. The key ch
Externí odkaz:
http://arxiv.org/abs/2202.02901
This paper focuses on EEG-based visual recognition, aiming to predict the visual object class observed by a subject based on his/her EEG signals. One of the main challenges is the large variation between signals from different subjects. It limits rec
Externí odkaz:
http://arxiv.org/abs/2110.13470
Domain generalization aims to enhance the model robustness against domain shift without accessing the target domain. Since the available source domains for training are limited, recent approaches focus on generating samples of novel domains. Neverthe
Externí odkaz:
http://arxiv.org/abs/2108.08596
Style transfer aims to reproduce content images with the styles from reference images. Existing universal style transfer methods successfully deliver arbitrary styles to original images either in an artistic or a photo-realistic way. However, the ran
Externí odkaz:
http://arxiv.org/abs/2108.04441
Face aging is the task aiming to translate the faces in input images to designated ages. To simplify the problem, previous methods have limited themselves only able to produce discrete age groups, each of which consists of ten years. Consequently, th
Externí odkaz:
http://arxiv.org/abs/2102.13318
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