8 citations · 18 across the 11 of their papers we have counts for
13 papers
Efficient Human-Contact Representation for Human-Scene Interaction
Nghia Vu, Tuong Do, Binh X. Nguyen +2
Human-scene interaction is an active research topic with several industrial applications in virtual reality, gaming, robotics, and surveillance. Despite significant progress in net…
AeroScene: Progressive Scene Synthesis for Aerial Robotics
Nghia Vu, Tuong Do, Dzung Tran +6
Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This gap is more evident in drone si…
AffordMatcher: Affordance Learning in 3D Scenes from Visual Signifiers
Nghia Vu, Tuong Do, Khang Nguyen +8
Affordance learning is a complex challenge in many applications, where existing approaches primarily focus on the geometric structures, visual knowledge, and affordance labels of o…
Lightweight Temporal Transformer Decomposition for Federated Autonomous Driving
Tuong Do, Binh X. Nguyen, Quang D. Tran +3
Traditional vision-based autonomous driving systems often face difficulties in navigating complex environments when relying solely on single-image inputs. To overcome this limitati…
Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss
Tuong Do, Binh X. Nguyen, Hien Nguyen +4
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 autonomou…
Reducing Training Time in Cross-Silo Federated Learning using Multigraph Topology
Tuong Do, Binh X. Nguyen, Vuong Pham +4
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 i…
Deep Federated Learning for Autonomous Driving
Anh Nguyen, Tuong Do, Minh Tran +5
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 w…
Coarse-to-Fine Reasoning for Visual Question Answering
Binh X. Nguyen, Tuong Do, Huy Tran +3
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 met…
Multiple Meta-model Quantifying for Medical Visual Question Answering
Tuong Do, Binh X. Nguyen, Erman Tjiputra +3
Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task. However, most of the exi…
Graph-based Person Signature for Person Re-Identifications
Binh X. Nguyen, Binh D. Nguyen, Tuong Do +3
The task of person re-identification (ReID) is to match images of the same person over multiple non-overlapping camera views. Due to the variations in visual factors, previous work…
Multiple interaction learning with question-type prior knowledge for constraining answer search space in visual question answering
Tuong Do, Binh X. Nguyen, Huy Tran +3
Different approaches have been proposed to Visual Question Answering (VQA). However, few works are aware of the behaviors of varying joint modality methods over question type prior…
Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach for Feature Embedding
Binh X. Nguyen, Binh D. Nguyen, Gustavo Carneiro +3
Unsupervised Deep Distance Metric Learning (UDML) aims to learn sample similarities in the embedding space from an unlabeled dataset. Traditional UDML methods usually use the tripl…
Overcoming Data Limitation in Medical Visual Question Answering
Binh D. Nguyen, Thanh-Toan Do, Binh X. Nguyen +3
Traditional approaches for Visual Question Answering (VQA) require large amount of labeled data for training. Unfortunately, such large scale data is usually not available for medi…