Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Tran, Quang D."'
Generating group dance motion from the music is a challenging task with several industrial applications. Although several methods have been proposed to tackle this problem, most of them prioritize optimizing the fidelity in dancing movement, constrai
Externí odkaz:
http://arxiv.org/abs/2407.18839
Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications. The ability to generate synchronized and visually appealing group dance motions that are aligned with music op
Externí odkaz:
http://arxiv.org/abs/2310.18986
Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group rema
Externí odkaz:
http://arxiv.org/abs/2303.12337
Autor:
Pham, Trong-Thang, Le, Nhat, Do, Tuong, Nguyen, Hung, Tjiputra, Erman, Tran, Quang D., Nguyen, Anh
Audio-driven talking head animation is a challenging research topic with many real-world applications. Recent works have focused on creating photo-realistic 2D animation, while learning different talking or singing styles remains an open problem. In
Externí odkaz:
http://arxiv.org/abs/2303.09799
Autor:
Do, Tuong, Nguyen, Binh X., Nguyen, Hien, Tjiputra, Erman, Tran, Quang D., Chiu, Te-Chuan, Nguyen, Anh
Federated learning has been widely applied in autonomous driving since it enables training a learning model among vehicles without sharing users' data. However, data from autonomous vehicles usually suffer from the non-independent-and-identically-dis
Externí odkaz:
http://arxiv.org/abs/2303.06305
Autor:
Do, Tuong, Nguyen, Binh X., Pham, Vuong, Tran, Toan, Tjiputra, Erman, Tran, Quang D., Nguyen, Anh
Federated learning is an active research topic since it enables several participants to jointly train a model without sharing local data. Currently, cross-silo federated learning is a popular training setting that utilizes a few hundred reliable data
Externí odkaz:
http://arxiv.org/abs/2207.09657
Extracting discriminative features plays a crucial role in the fine-grained visual classification task. Most of the existing methods focus on developing attention or augmentation mechanisms to achieve this goal. However, addressing the ambiguity in t
Externí odkaz:
http://arxiv.org/abs/2205.10529
Autor:
Nguyen, Anh, Do, Tuong, Tran, Minh, Nguyen, Binh X., Duong, Chien, Phan, Tu, Tjiputra, Erman, Tran, Quang D.
Autonomous driving is an active research topic in both academia and industry. However, most of the existing solutions focus on improving the accuracy by training learnable models with centralized large-scale data. Therefore, these methods do not take
Externí odkaz:
http://arxiv.org/abs/2110.05754
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task. However, most of the existing VQA methods focus on attention mechanisms or visual relations for reasoning t
Externí odkaz:
http://arxiv.org/abs/2110.02526
Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between th
Externí odkaz:
http://arxiv.org/abs/2110.01293