Trong-Tung Nguyen

Trong-Tung Nguyen received his B.Sc. degree (top 2% students) in Computer Science from Advanced Program In Computer Science at the University of Science, VNU-HCM in 2022 under supervision of Prof. Minh-Triet Tran.

During the time at university, he worked at Cinnamon AI company as AI Researcher (2020-2021). After that, he joined VinUni-Illinois Smart Health Center (VinUniversity-UIUC) as research assistant and worked closely with Dr.Huy-Hieu Pham and Prof. Minh Do on individual research about AI healthcare problem. At the same time, he also worked as research assistant on a research project with Monash University under supervision of Prof. Wray Buntine.

Trong-Tung Nguyen is actively looking for fully-funded Ph.D. position in Computer Science. His research interests include applications of Computer Vision, Multimodal Learning, Explainable AI, and Deep Incremental Learning.

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Classifying pill categories from real-world images is crucial for various smart healthcare applications. Although existing approaches in image classification might achieve a good performance on fixed pill categories, they fail to handle novel instances of pill categories that are frequently presented to the learning algorithm. To this end, a trivial solution is to train the model with novel classes. However, this may result in a phenomenon known as catastrophic forgetting, in which the system forgets what it learned in previous classes. In this paper, we address this challenge by introducing the class incremental learning (CIL) ability to traditional pill image classification systems. Specifically, we propose a novel incremental multi-stream intermediate fusion framework enabling incorporation of an additional guidance information stream that best matches the domain of the problem into various state-of-the-art CIL methods. From this framework, we consider color-specific information of pill images as a guidance stream and devise an approach, namely “Color Guidance with Multi-stream intermediate fusion”(CG-IMIF) for solving CIL pill image classification task. We conduct comprehensive experiments on real-world incremental pill image classification dataset, namely VAIPE-PCIL, and find that the CG-IMIF consistently outperforms several state-of-the-art methods by a large margin in different task settings.

Our HCMUS team engaged in the challenge with the main contribution of improving the classification, aiming to intensify the effectiveness of our previous method in 2020. Our best run ranked second in the Sports Video Task with 68.8% of accuracy

By defining the interaction between humans and objects via body-part regions, we decompose the second stage into pair matching and relationship prediction, whereas the first stage is used for detecting objects and humans in an image. Moreover, we introduce two novel features: body-part aware features in pair matching and object affordance features in relationship prediction to overcome the current limitations of other methods. Our proposed method can achieve state-of-the-art results for two-stage methods on a common HOI dataset: PIC HOI-A 2019 with 0.6617 mAP scores. Our methods can now be integrated into different intelligent visual analysis tasks such as human activity analysis, life-logging, and visual question answering.

In this paper, we introduce our Intelligent Traffic Analysis Software Kit (iTASK) to tackle three challenging problems: vehicle flow counting, vehicle re-identification, and abnormal event detection

We proposed an architecture to effectively make use of the network of citizens to deal with city anomalies. At the center of our architecture is a neural network that automatically classify incoming image data and use this information to assist anomaly handling efforts.

Besides doing research, I also participated in other projects building interesting application. Here are some of my highlighted projects.

VAIPE is a project funded by VinIF, composed of VinUniversity, Hanoi University of Science & Technology (HUST), The University of Massachusetts Boston (UMass Boston), and The University of South Florida (USF). The project aims to build an intelligent healthcare system to assist users in collecting, managing, and analyzing their health-related data. Our system enables users to collect heterogeneous data captured from multiple sources using a convenient smartphone camera, provides visualizations of analytical and predicted results, and includes functions to support users, for example, reminding of medication schedules and warning of early-disease risks. VAIPE is AI-assisted and involves original research and development of several key modules. For more information, please visit our website.

I'm working with my teammate - Truong-Phat Nguyen for a solution to ICDAR 2019 Robust Reading Challenge on Scanned Receipts OCR and Information Extraction.

This application provide user a platform to perform visual search with various query options including text, image, and sketch

●   [September. 2022]   One paper "Multi-stream Fusion for Class Incremental Learning in Pill Image Classification" got accepted to ACCV2022 - The 16th Asian Conference on Computer Vision.

●   [Jul. 2022]   I was invited to serve as a Reviewer for The 19th Pacific Rim International Conference on Artificial Intelligence (PRCICAI 2022).

●   [Jul. 2022]   I started to work as Research Assistant for a research project about Change Point Detection Problem from Monash University under supervision of Prof. Wray Buntine and Dr. Mahsa Salehi .

●   [May. 2022]   I gave a talk on my research topic "Multi-stream Class Incremental Learning for Pill Image Identification" at VinUni-Illinois Smart Health Center Seminar Series. .

●   [April. 2022]   I received my excellent B.Sc. degree in Computer Science (among top 2% students, GPA: 3.9/4.0 or 9.24/10.0) at University Of Science, VNU-HCM. .

●   [Feb. 2022]   I worked as Teaching Assistant for the course COMP2050-Artificial Intelligence at VinUniversity, taught by Prof. Wray Buntine .

●   [Dec. 2021]   I joined VinUni-Illinois Smart Health Center as Research Assistant under supervision of Dr. Huy-Hieu Pham and Prof. Minh Do. My research focus on the application of class incremental learning problem on pill image domain. This research is a part of VAIPE project: AI-assisted IoT-enabled smart, optimal, and Protective Healthcare monitoring and supporting system for Vietnamese.

●   [October. 2021]   Our working note paper was submitted at MediaEval 2021 workshop.

●   [April. 2020]   I started to work as part-time AI Researcher at Cinnamon AI company.

●   [March. 2020]   One paper "iTASK - Intelligent Traffic Analysis Software Kit" got accepted to CVPR2020-AI City Challenge Workshop.

●   [December. 2019]   One paper "Visual Assistant for Crowdsourced Anomaly Event Recognition in Smart City" got accepted to The 10th International Symposium on Information and Communication Technology.

●   [October. 2019]   Our team participated in National Codewar Competition as finalist by FSoft 2019.

●   [September. 2019]   I received full scholarship to participate in the Data Science track training program at CoderSchool.

• Incentive Scholarship - Honda Award 2021
• VinIF Scholarship 2021 for Higher Education.
• Top 2 challenge winners in Sport Task-Stroke Classification Substask of Mediaeval Challenge 2021.
• Top 6 challenge winners in track 1 of AI City Challenge - CVPR 2020.
• Cinnamon AI Bootcamp For Student Awards - Cinnamon AI 2020.
• Best Presentation Award at SoICT Conference - Hanoi University of Science and Technology (HUST) 2019.
• Finalist at National Codewar Competition by FSoft - 2019.
• Global Software Talent Scholarship by FPT Software - 2019.
• Data Scientist training Scholarship from Coderschool - 2019.
• Runner-up at UNESCO Hackathon by Fossasia - 2018.
• Silver Medal in Mathematics at 30/4 Olympic Competition for Southern Region - 2016.
• Silver Medal in Mathematics at 30/4 Olympic Competition for Southern Region - 2015.
• 3rd Prize in Mathematics for Gifted Student in Ho Chi Minh City - 2014.

• Invited Talk at Smart Health Center Series on "Multi-stream Class Incremental Learning for Pill Image Identification".
• Invited Talk at Cinnamon AI Bootcamp Graduation Day - 2020.
• Cinnamon AI Technical Blog on "Information Extraction for receipts with AI agents" - 2020.

• Teaching Assistant for COMP2050-Artificial Intelligence at VinUniversity

• Served as Reviewer of The 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI2022).