Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Junehyoung Kwon"'
Publikováno v:
IEEE Access, Vol 12, Pp 99989-100004 (2024)
Computer vision has emerged as a promising tool for improving safety at construction sites through automatic scene recognition. However, traditional approaches require significant labor-intensive and time-consuming efforts for annotations. Although w
Externí odkaz:
https://doaj.org/article/466e871efd914719822b58f1a62b95a9
Publikováno v:
IEEE Access, Vol 11, Pp 140496-140505 (2023)
Facial expression recognition (FER) has been extensively studied in various applications over the past few years. However, in real facial expression datasets, labels can become noisy due to the ambiguity of expressions, the similarity between classes
Externí odkaz:
https://doaj.org/article/481e228e61434c6789aed54ca23a18a6
Publikováno v:
IEEE Access, Vol 11, Pp 115644-115653 (2023)
Deep-learning models often struggle to generalize well to unseen domains because of the distribution shift between the training and real-world data. Domain generalization aims to train models that can acquire general features from data across differe
Externí odkaz:
https://doaj.org/article/9d8306b145884fdc83098c1d71dda790
Publikováno v:
PLoS ONE, Vol 17, Iss 10 (2022)
Among the various types of data augmentation strategies, the mixup-based approach has been particularly studied. However, in existing mixup-based approaches, object loss and label mismatching can occur if random patches are utilized when constructing
Externí odkaz:
https://doaj.org/article/d43c7f59000b4b7eafea64070cdafe1c
Publikováno v:
TECHART: Journal of Arts and Imaging Science. 9:27-30
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::83bd2b42a05e611456f8cfce79fc1542
https://doi.org/10.15323/techart.2022.2.9.1.27
https://doi.org/10.15323/techart.2022.2.9.1.27
Publikováno v:
Engineering Applications of Artificial Intelligence. 119:105706
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6aa2027df8010d9ab512936cfbcc770
https://doi.org/10.1016/j.engappai.2022.105706
https://doi.org/10.1016/j.engappai.2022.105706