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 • 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
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 • 9 Sep 2020 • Binh X. Nguyen, Binh D. Nguyen, Gustavo Carneiro, Erman Tjiputra, Quang D. Tran, Thanh-Toan Do
Based on pseudo labels, we propose a novel unsupervised metric loss which enforces the positive concentration and negative separation of samples in the embedding space.
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)