Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Nguyen, Phuong D."'
Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream gr
Externí odkaz:
http://arxiv.org/abs/2307.01844
In this study, we emphasize the integration of a pre-trained MICA model with an imperfect face dataset, employing a self-supervised learning approach. We present an innovative method for regenerating flawed facial structures, yielding 3D printable ou
Externí odkaz:
http://arxiv.org/abs/2304.04060
Autor:
Nguyen, Phuong D., Le, Thinh D., Nguyen, Duong Q., Nguyen, Thanh Q., Chou, Li-Wei, Nguyen-Xuan, H.
This study explores the potential of a fully convolutional mesh autoencoder model for regenerating 3D nature faces with the presence of imperfect areas. We utilize deep learning approaches in graph processing and analysis to investigate the capabilit
Externí odkaz:
http://arxiv.org/abs/2303.14381
Autor:
Eppe, Manfred, Gumbsch, Christian, Kerzel, Matthias, Nguyen, Phuong D. H., Butz, Martin V., Wermter, Stefan
Publikováno v:
Nature Machine Intelligence, 4(1) (2022)
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. Hierarchical reinforcement learning is a promising computational approa
Externí odkaz:
http://arxiv.org/abs/2208.08731
Autor:
Eppe, Manfred, Gumbsch, Christian, Kerzel, Matthias, Nguyen, Phuong D. H., Butz, Martin V., Wermter, Stefan
Publikováno v:
Nature Machine Intelligence, 4(1) (2022)
Cognitive Psychology and related disciplines have identified several critical mechanisms that enable intelligent biological agents to learn to solve complex problems. There exists pressing evidence that the cognitive mechanisms that enable problem-so
Externí odkaz:
http://arxiv.org/abs/2012.10147
Autor:
Nguyen, Phuong D. H., Georgie, Yasmin Kim, Kayhan, Ezgi, Eppe, Manfred, Hafner, Verena Vanessa, Wermter, Stefan
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than
Externí odkaz:
http://arxiv.org/abs/2011.12860
Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper,
Externí odkaz:
http://arxiv.org/abs/2011.06985
Reinforcement learning is a promising method to accomplish robotic control tasks. The task of playing musical instruments is, however, largely unexplored because it involves the challenge of achieving sequential goals - melodies - that have a tempora
Externí odkaz:
http://arxiv.org/abs/2011.05715
Recently, computer-aided diagnostic systems (CADs) that could automatically interpret medical images effectively have been the emerging subject of recent academic attention. For radiographs, several deep learning-based systems or models have been dev
Externí odkaz:
http://arxiv.org/abs/2009.05951
We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic p
Externí odkaz:
http://arxiv.org/abs/2008.06828