no code implementations • 29 Oct 2023 • Nhat Le, Tuong Do, Khoa Do, Hien Nguyen, Erman Tjiputra, Quang D. Tran, Anh Nguyen
Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications.
no code implementations • CVPR 2023 • Nhat Le, Thang Pham, Tuong Do, Erman Tjiputra, Quang D. Tran, Anh Nguyen
The proposed dataset consists of 16. 7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres.
1 code implementation • 17 Mar 2023 • Trong-Thang Pham, Nhat Le, Tuong Do, Hung Nguyen, Erman Tjiputra, Quang D. Tran, Anh Nguyen
In this paper, we present a new method to generate talking head animation with learnable style references.
1 code implementation • ICCV 2023 • Tuong Do, Binh X. Nguyen, Vuong Pham, Toan Tran, Erman Tjiputra, Quang D. Tran, Anh Nguyen
In this paper, we present a new multigraph topology for cross-silo federated learning.
1 code implementation • 21 May 2022 • Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, Anh Nguyen
We show that by effectively addressing the ambiguity in the top-k prediction classes, our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets.
Ranked #4 on Fine-Grained Image Classification on Stanford Dogs
1 code implementation • 12 Oct 2021 • Anh Nguyen, Tuong Do, Minh Tran, Binh X. Nguyen, Chien Duong, Tu Phan, Erman Tjiputra, Quang D. Tran
We design a new Federated Autonomous Driving network (FADNet) that can improve the model stability, ensure convergence, and handle imbalanced data distribution problems while is being trained with federated learning methods.
2 code implementations • 6 Oct 2021 • Binh X. Nguyen, Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, Anh Nguyen
Bridging the semantic gap between image and question is an important step to improve the accuracy of the Visual Question Answering (VQA) task.
Ranked #1 on Visual Question Answering (VQA) on GQA test-dev
1 code implementation • 4 Oct 2021 • Minh Q. Tran, Tuong Do, Huy Tran, Erman Tjiputra, Quang D. Tran, Anh Nguyen
We design the student network such as it is light-weight and well suitable for deployment on a typical CPU.
1 code implementation • 7 Sep 2021 • Huy Q. Vo, Tuong Do, Vi C. Pham, Duy Nguyen, An T. Duong, Quang D. Tran
This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH".
2 code implementations • 19 May 2021 • Tuong Do, Binh X. Nguyen, Erman Tjiputra, Minh Tran, Quang D. Tran, Anh Nguyen
However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized.
Ranked #5 on Medical Visual Question Answering on PathVQA
1 code implementation • 14 Apr 2021 • Binh X. Nguyen, Binh D. Nguyen, Tuong Do, Erman Tjiputra, Quang D. Tran, Anh Nguyen
In this paper, we propose a new method to effectively aggregate detailed person descriptions (attributes labels) and visual features (body parts and global features) into a graph, namely Graph-based Person Signature, and utilize Graph Convolutional Networks to learn the topological structure of the visual signature of a person.
Ranked #48 on Person Re-Identification on DukeMTMC-reID
1 code implementation • 23 Sep 2020 • Tuong Do, Binh X. Nguyen, Huy Tran, Erman Tjiputra, Quang D. Tran, Thanh-Toan Do
Different approaches have been proposed to Visual Question Answering (VQA).
1 code implementation • ICCV 2019 • Tuong Do, Thanh-Toan Do, Huy Tran, Erman Tjiputra, Quang D. Tran
In Visual Question Answering (VQA), answers have a great correlation with question meaning and visual contents.
Ranked #2 on Visual Question Answering (VQA) on TDIUC
2 code implementations • 26 Sep 2019 • Binh D. Nguyen, Thanh-Toan Do, Binh X. Nguyen, Tuong Do, Erman Tjiputra, Quang D. Tran
Traditional approaches for Visual Question Answering (VQA) require large amount of labeled data for training.
Ranked #13 on Medical Visual Question Answering on VQA-RAD (using extra training data)