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pro vyhledávání: '"Li, Yante"'
Autor:
Varanka, Tuomas, Khor, Huai-Qian, Li, Yante, Wei, Mengting, Kung, Hanwei, Sebe, Nicu, Zhao, Guoying
Generative models have surged in popularity recently due to their ability to produce high-quality images and video. However, steering these models to produce images with specific attributes and precise control remains challenging. Humans, particularl
Externí odkaz:
http://arxiv.org/abs/2407.20175
Micro-expressions have drawn increasing interest lately due to various potential applications. The task is, however, difficult as it incorporates many challenges from the fields of computer vision, machine learning and emotional sciences. Due to the
Externí odkaz:
http://arxiv.org/abs/2211.11425
Autor:
Li, Yante, Liu, Yang, Nguyen, KhÁnh, Shi, Henglin, Vuorenmaa, Eija, Jarvela, Sanna, Zhao, Guoying
Collaborative learning is an educational approach that enhances learning through shared goals and working together. Interaction and regulation are two essential factors related to the success of collaborative learning. Since the information from vari
Externí odkaz:
http://arxiv.org/abs/2210.05419
Micro-expression recognition (MER) is valuable because micro-expressions (MEs) can reveal genuine emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged
Externí odkaz:
http://arxiv.org/abs/2205.00380
Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in medical treatment, national security, interrogations and many human-computer interaction systems. Ea
Externí odkaz:
http://arxiv.org/abs/2107.02823
As one of the most important affective signals, facial affect analysis (FAA) is essential for developing human-computer interaction systems. Early methods focus on extracting appearance and geometry features associated with human affects while ignori
Externí odkaz:
http://arxiv.org/abs/2103.15599
Akademický článek
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Action Unit (AU) detection plays an important role for facial expression recognition. To the best of our knowledge, there is little research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Microe
Externí odkaz:
http://arxiv.org/abs/1907.05023
Akademický článek
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Publikováno v:
In Neurocomputing 14 May 2021 436:221-231
