Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Hong, Sungeun"'
Existing face restoration models have relied on general assessment metrics that do not consider the characteristics of facial regions. Recent works have therefore assessed their methods using human studies, which is not scalable and involves signific
Externí odkaz:
http://arxiv.org/abs/2211.07077
Crowd counting research has made significant advancements in real-world applications, but it remains a formidable challenge in cross-modal settings. Most existing methods rely solely on the optical features of RGB images, ignoring the feasibility of
Externí odkaz:
http://arxiv.org/abs/2210.10392
Active domain adaptation (ADA) studies have mainly addressed query selection while following existing domain adaptation strategies. However, we argue that it is critical to consider not only query selection criteria but also domain adaptation strateg
Externí odkaz:
http://arxiv.org/abs/2204.11665
Publikováno v:
In Pattern Recognition Letters September 2024 185:197-202
Publikováno v:
In Neurocomputing 21 January 2025 614
Publikováno v:
In Image and Vision Computing July 2024 147
Partial Adaptation (PDA) addresses a practical scenario in which the target domain contains only a subset of classes in the source domain. While PDA should take into account both class-level and sample-level to mitigate negative transfer, current app
Externí odkaz:
http://arxiv.org/abs/2008.03111
Domain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such an assumption is rarely plausible in the real-world and possibly causes data-privacy issues, especially when the label
Externí odkaz:
http://arxiv.org/abs/2007.01524
This paper proposes key instance selection based on video saliency covering objectness and dynamics for unsupervised video object segmentation (UVOS). Our method takes frames sequentially and extracts object proposals with corresponding masks for eac
Externí odkaz:
http://arxiv.org/abs/1906.07851
Publikováno v:
In Neural Networks April 2023 161:670-681