I obtained my MPhil degree in Computer Science and Engineering from The Hong Kong University of Science and Technology (HKUST), where I was advised by Prof. Sai-Kit Yeung. Prior to that, I earned my B.S. in Computer Science (Advanced Program) with highest distinction (GPA: 3.93/4.0) from the University of Science, VNU - HCM, under the guidance of Prof. Minh-Triet Tran.

My research focuses on multimodal understanding, building upon a foundation in computer vision (detection & segmentation). I explored these areas during my MPhil at HKUST and now apply them to multimodal/multitask generation model at Huawei HKRC as AI Research Engineer, contributing to the MindONE project. My core interests include:

• Vision-language foundation model for navigation, interaction and action: Creating agents that can understand natural language instructions to navigate, interact and act with real-world or virtual environments.
• Multimodal event reasoning: Developing models that precisely locate and describe complex events in long-form videos using visual and auditory cues.

🔥 News

• 2025.06 🏆 Best Poster Presentation Award at AVSTC - Thrilled to receive this prestigious joint recognition from the Australian Government 🇦🇺, University of Technology Sydney (UTS) 🎓, and PTIT for my research presentation! A fantastic validation of my research! ✨
• 2024.11: 🚀 New Role! Excited to announce that I’ve joined Huawei Hong Kong Research Center as an AI Research Engineer, where I’ll be contributing to cutting-edge AIGC (AI-Generated Content) projects. Looking forward to this new journey! 💫
• 2024.10: 🎯 Paper Accepted! My first paper “VATEX” has been accepted to WACV 2025. Thrilled to share that this marks my debut as a co-first author in a top-tier computer vision conference! 🎉

💻 Experience

📝 Publications

WACV 2025

Vision-Aware Text Features in Referring Expression Segmentation: From Object Understanding to Context Understanding

Hai Nguyen-Truong, E-Ro Nguyen, Tuan-Anh Vu, Minh-Triet Tran, Binh-Son Hua, Sai-Kit Yeung

Project

• VATEX is a novel method for referring image segmentation that leverages vision-aware text features to improve text understanding. By decomposing language cues into object and context understanding, the model can better localize objects and interpret complex sentences, leading to significant performance gains.

ISBI 2022

SegTransVAE: Hybrid CNN - Transformer with Regularization for medical image segmentation

Hai Nguyen-Truong, Quan-Dung Pham, Nam Nguyen Phuong, Khoa NA Nguyen, Chanh DT Nguyen, Trung Bui, Steven QH Truong

Project

• SegTransVAE is the first work exploiting the hybrid architecture between CNN, Transformers with the Variational Autoencoder (VAE) branch to the network to reconstruct the input images jointly with segmentation.

🎖 Honors and Awards

🏆 Challenges and Competitions