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pro vyhledávání: '"Choi, Jongwon"'
This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos. We discovered that the generated facial videos suffer from the
Externí odkaz:
http://arxiv.org/abs/2403.06592
Autor:
Lee, Mingyu, Choi, Jongwon
We propose a text-guided variational image generation method to address the challenge of getting clean data for anomaly detection in industrial manufacturing. Our method utilizes text information about the target object, learned from extensive text l
Externí odkaz:
http://arxiv.org/abs/2403.06247
Autor:
Yoo, YoungJoon, Choi, Jongwon
This paper introduces a novel approach for topic modeling utilizing latent codebooks from Vector-Quantized Variational Auto-Encoder~(VQ-VAE), discretely encapsulating the rich information of the pre-trained embeddings such as the pre-trained language
Externí odkaz:
http://arxiv.org/abs/2312.11532
The class-wise training losses often diverge as a result of the various levels of intra-class and inter-class appearance variation, and we find that the diverging class-wise training losses cause the uncalibrated prediction with its reliability. To r
Externí odkaz:
http://arxiv.org/abs/2306.10989
Autor:
Jin, Heegon, Choi, Jongwon
Although transformer networks are recently employed in various vision tasks with outperforming performance, extensive training data and a lengthy training time are required to train a model to disregard an inductive bias. Using trainable links betwee
Externí odkaz:
http://arxiv.org/abs/2211.13852
In a recent paper [18] the authors proposed the first solution to the problem of designing a {\em globally exponentially stable} (GES) flux observer for the interior permanent magnet synchronous motor. However, the establishment of the stability proo
Externí odkaz:
http://arxiv.org/abs/2210.00190
Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise from the overfitting issue, which we discover from our own analysis and
Externí odkaz:
http://arxiv.org/abs/2202.03347
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news. To prevent such cases, vigorous research is conducted to distinguish t
Externí odkaz:
http://arxiv.org/abs/2111.06575
Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their huge success in image classification, the number of images to be mixe
Externí odkaz:
http://arxiv.org/abs/2110.04248
In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent such harm, we propose an anti-spoofing method using the paired rgb im
Externí odkaz:
http://arxiv.org/abs/2110.04066