publications

2025

1. IEEE FG

   

   MultiSensor-Home: A Wide-area Multi-modal Multi-view Dataset for Action Recognition and Transformer-based Sensor Fusion

   Trung Thanh Nguyen, Yasutomo Kawanishi, Vijay John, and 2 more authors

   In Proceedings of the 19th IEEE International Conference on Automatic Face and Gesture Recognition, 2025

   arXiv Bib Code

   @inproceedings{nguyen2025multisensor,
     note = {International-Conference},
     title = {MultiSensor-Home: A Wide-area Multi-modal Multi-view Dataset for Action Recognition and Transformer-based Sensor Fusion},
     author = {Nguyen, Trung Thanh and Kawanishi, Yasutomo and John, Vijay and Komamizu, Takahiro and Ide, Ichiro},
     booktitle = {Proceedings of the 19th IEEE International Conference on Automatic Face and Gesture Recognition},
     year = {2025},
     primaryclass = {cs.CV},
   }

2. IEEE/CVF WACV

   

   CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach

   Dac Thai Nguyen, Trung Thanh Nguyen, Huu Tien Nguyen, and 5 more authors

   In Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision, 2025

   Abs arXiv Bib Code

   Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT scanners are more widely available and significantly less expensive. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients. Our contributions are twofold: First, we introduce a conditional diffusion model named CPDM, which, to our knowledge, is one of the initial attempts to employ a diffusion model for translating from CT to PET images. Second, we provide the largest CT-PET dataset to date, comprising 2,028,628 paired CT-PET images, which facilitates the training and evaluation of CT-to-PET translation models. For the CPDM model, we incorporate domain knowledge to develop two conditional maps: the Attention map and the Attenuation map. The former helps the diffusion process focus on areas of interest, while the latter improves PET data correction and ensures accurate diagnostic information. Experimental evaluations across various benchmarks demonstrate that CPDM surpasses existing methods in generating high-quality PET images in terms of multiple metrics.

   @inproceedings{Nguyen2025CTPET,
     note = {International-Conference},
     author = {Nguyen, Dac Thai and Nguyen, Trung Thanh and Nguyen, Huu Tien and Nguyen, Thanh Trung and Pham, Huy Hieu and Nguyen, Thanh Hung and Nguyen, Truong Thao and Nguyen, Phi Le},
     title = {CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach},
     booktitle = {Proceedings of the 2025 IEEE/CVF Winter Conference on Applications of Computer Vision},
     year = {2025},
     publisher = {IEEE},
   }

2024

1. ACM MMAsia

   

   Action Selection Learning for Multi-label Multi-view Action Recognition

   Trung Thanh Nguyen, Yasutomo Kawanishi, Takahiro Komamizu, and 1 more author

   In Proceedings of the 6th ACM International Conference on Multimedia in Asia, 2024

   Abs DOI arXiv Bib HTML Code

   Multi-label multi-view action recognition aims to recognize multiple concurrent or sequential actions from untrimmed videos captured by multiple cameras. Existing work has focused on multi-view action recognition in a narrow area with strong labels available, where the onset and offset of each action are labeled at the frame-level. This study focuses on real-world scenarios where cameras are distributed to capture a wide-range area with only weak labels available at the video-level. We propose a method named Multi-view Action Selection Learning (MultiASL), which leverages action selection learning to enhance view fusion by selecting the most useful information from different viewpoints. The proposed method includes a Multi-view Spatial-Temporal Transformer video encoder to extract spatial and temporal features from multi-viewpoint videos. Action Selection Learning is employed at the frame-level, using pseudo ground-truth obtained from weak labels at the video-level, to identify the most relevant frames for action recognition. Experiments in a real-world office environment using the MM-Office dataset demonstrate the superior performance of the proposed method compared to existing methods.

   @inproceedings{10.1145/3696409.3700211,
     note = {International-Conference},
     author = {Nguyen, Trung Thanh and Kawanishi, Yasutomo and Komamizu, Takahiro and Ide, Ichiro},
     title = {Action Selection Learning for Multi-label Multi-view Action Recognition},
     year = {2024},
     isbn = {9798400712739},
     doi = {10.1145/3696409.3700211},
     booktitle = {Proceedings of the 6th ACM International Conference on Multimedia in Asia},
   }

2. IEEE NCA

   

   FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization

   Manh Duong Nguyen, Trung Thanh Nguyen, Huy Hieu Pham, and 3 more authors

   In Proceedings of the 22nd IEEE International Symposium on Network Computing and Applications, 2024

   arXiv Bib

   @inproceedings{Nguyen2024FedMAC,
     note = {International-Conference},
     author = {Nguyen, Manh Duong and Nguyen, Trung Thanh and Pham, Huy Hieu and Hoang, Trong Nghia and Nguyen, Phi Le and Huynh, Thanh Trung},
     title = {FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularization},
     booktitle = {Proceedings of the 22nd IEEE International Symposium on Network Computing and Applications},
     year = {2024},
     publisher = {IEEE},
   }

3. IEEE NCA

   

   FedCert: Federated Accuracy Certification

   Minh Hieu Nguyen, Huu Tien Nguyen, Trung Thanh Nguyen, and 4 more authors

   In Proceedings of the 22nd IEEE International Symposium on Network Computing and Applications, 2024

   arXiv Bib

   @inproceedings{Nguyen2024FedCert,
     note = {International-Conference},
     author = {Nguyen, Minh Hieu and Nguyen, Huu Tien and Nguyen, Trung Thanh and Nguyen, Manh Duong and Hoang, Trong Nghia and Nguyen, Truong Thao and Nguyen, Phi Le},
     title = {FedCert: Federated Accuracy Certification},
     booktitle = {Proceedings of the 22nd IEEE International Symposium on Network Computing and Applications},
     year = {2024},
     publisher = {IEEE},
   }

4. IEEE FG

   

   One-Stage Open-Vocabulary Temporal Action Detection Leveraging Temporal Multi-Scale and Action Label Features

   Trung Thanh Nguyen, Yasutomo Kawanishi, Takahiro Komamizu, and 1 more author

   In Proceedings of the 18th IEEE International Conference on Automatic Face and Gesture Recognition, 2024

   Abs DOI arXiv Bib

   Open-vocabulary Temporal Action Detection (Open-vocab TAD) is an advanced video analysis approach that expands Closed-vocabulary Temporal Action Detection (Closed-vocab TAD) capabilities. Closed-vocab TAD is typically confined to localizing and classifying actions based on a predefined set of categories. In contrast, Open-vocab TAD goes further and is not limited to these predefined categories. This is particularly useful in real-world scenarios where the variety of actions in videos can be vast and not always predictable. The prevalent methods in Open-vocab TAD typically employ a 2-stage approach, which involves generating action proposals and then identifying those actions. However, errors made during the first stage can adversely affect the subsequent action identification accuracy. Additionally, existing studies face challenges in handling actions of different durations owing to the use of fixed temporal processing methods. Therefore, we propose a 1-stage approach consisting of two primary modules: Multi-scale Video Analysis (MVA) and Video-Text Alignment (VTA). The MVA module captures actions at varying temporal resolutions, overcoming the challenge of detecting actions with diverse durations. The VTA module leverages the synergy between visual and textual modalities to precisely align video segments with corresponding action labels, a critical step for accurate action identification in Open-vocab scenarios. Evaluations on widely recognized datasets THUMOS14 and ActivityNet-1.3, showed that the proposed method achieved superior results compared to the other methods in both Open-vocab and Closed-vocab settings. This serves as a strong demonstration of the effectiveness of the proposed method in the TAD task.

   @inproceedings{10581896,
     note = {International-Conference},
     author = {Nguyen, Trung Thanh and Kawanishi, Yasutomo and Komamizu, Takahiro and Ide, Ichiro},
     booktitle = {Proceedings of the 18th IEEE International Conference on Automatic Face and Gesture Recognition},
     title = {One-Stage Open-Vocabulary Temporal Action Detection Leveraging Temporal Multi-Scale and Action Label Features},
     year = {2024},
     doi = {10.1109/FG59268.2024.10581896},
   }

2022

1. PRICAI

   A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning

   Trung Thanh Nguyen, Hoang Dang Nguyen, Thanh Hung Nguyen, and 3 more authors

   In Proceedings of the 2022 Pacific Rim International Conference on Artificial Intelligence, 2022

   DOI arXiv Bib

   @inproceedings{nguyen2022novel,
     note = {International-Conference},
     title = {A Novel Approach for Pill-Prescription Matching with GNN Assistance and Contrastive Learning},
     author = {Nguyen, Trung Thanh and Nguyen, Hoang Dang and Nguyen, Thanh Hung and Pham, Huy Hieu and Ide, Ichiro and Nguyen, Phi Le},
     booktitle = {Proceedings of the 2022 Pacific Rim International Conference on Artificial Intelligence},
     pages = {261--274},
     year = {2022},
     organization = {Springer},
     doi = {10.1007/978-3-031-20862-1_19},
   }

2. ICPP

   FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

   Nang Hung Nguyen, Phi Le Nguyen, Thuy Dung Nguyen, and 5 more authors

   In Proceedings of the 51st International Conference on Parallel Processing, 2022

   DOI arXiv Bib

   @inproceedings{nguyen2022feddrl,
     note = {International-Conference},
     title = {FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning},
     author = {Nguyen, Nang Hung and Nguyen, Phi Le and Nguyen, Thuy Dung and Nguyen, Trung Thanh and Nguyen, Duc Long and Nguyen, Thanh Hung and Pham, Huy Hieu and Truong, Thao Nguyen},
     booktitle = {Proceedings of the 51st International Conference on Parallel Processing},
     year = {2022},
     doi = {10.1145/3545008.3545085},
   }

3. IEEE WCNC

   Deep Reinforcement Learning-based Offloading for Latency Minimization in 3-tier V2X Networks

   Hieu Dinh, Nang Hung Nguyen, Trung Thanh Nguyen, and 3 more authors

   In Proceedings of the 2022 IEEE Wireless Communications and Networking Conference, 2022

   DOI Bib

   @inproceedings{9771583,
     note = {International-Conference},
     author = {Dinh, Hieu and Nguyen, Nang Hung and Nguyen, Trung Thanh and Nguyen, Thanh Hung and Nguyen, Truong Thao and Le Nguyen, Phi},
     booktitle = {Proceedings of the 2022 IEEE Wireless Communications and Networking Conference},
     title = {Deep Reinforcement Learning-based Offloading for Latency Minimization in 3-tier V2X Networks},
     year = {2022},
     pages = {1803-1808},
     doi = {10.1109/WCNC51071.2022.9771583}
   }

2021

1. KSE

   Data Augmentation for Small Face Datasets and Face Verification by Generative Adversarial Networks Inversion

   Dinh Tan Nguyen, Cao Truong Tran, Trung Thanh Nguyen, and 4 more authors

   In Proceedings of the 13th International Conference on Knowledge and Systems Engineering, 2021

   DOI Bib

   @inproceedings{9648720,
     note = {International-Conference},
     author = {Nguyen, Dinh Tan and Tran, Cao Truong and Nguyen, Trung Thanh and Hoang, Cao Bao and Luu, Van Phu and Nguyen, Ba Ngoc and Cheong, Pou Ian},
     booktitle = {Proceedings of the 13th International Conference on Knowledge and Systems Engineering},
     title = {Data Augmentation for Small Face Datasets and Face Verification by Generative Adversarial Networks Inversion},
     year = {2021},
     pages = {1-6},
     doi = {10.1109/KSE53942.2021.9648720},
   }

2. IEEE IPCCC

   Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems

   Trung Thanh Nguyen, Truong Thao Nguyen, Tuan Anh Nguyen Dinh, and 2 more authors

   In Proceedings of the 2021 IEEE International Performance, Computing, and Communications Conference, 2021

   Abs DOI arXiv Bib

   We focus on real-time air quality monitoring systems that rely on devices installed on automobiles in this research. We investigate an opportunistic communication model in which devices can send the measured data directly to the air quality server through a 4G communication channel or via Wi-Fi to adjacent devices or the so-called Road Side Units deployed along the road. We aim to reduce 4G costs while assuring data latency, where the data latency is defined as the amount of time it takes for data to reach the server. We propose an offloading scheme that leverages Q-learning to accomplish the purpose. The experiment results show that our offloading method significantly cuts down around 40-50% of the 4G communication cost while keeping the latency of 99.5% packets smaller than the required threshold.

   @inproceedings{9679398,
     note = {International-Conference},
     author = {Nguyen, Trung Thanh and Thao Nguyen, Truong and Nguyen Dinh, Tuan Anh and Nguyen, Thanh Hung and Nguyen, Phi Le},
     booktitle = {Proceedings of the 2021 IEEE International Performance, Computing, and Communications Conference},
     title = {Q-learning-based Opportunistic Communication for Real-time Mobile Air Quality Monitoring Systems},
     year = {2021},
     doi = {10.1109/IPCCC51483.2021.9679398},
   }