Tien-Dat Tran

About

Tien-Dat Tran is an emerging researcher in computer vision, with a focused interest in hand detection, gesture recognition, and human-computer interaction. His most-cited work, "Robust Hand Detection Based on Convolutional Neural Network and Attention Module" (2022), introduces a novel architecture that integrates convolutional neural networks with attention mechanisms to improve the accuracy and robustness of hand detection in complex environments. This contribution is particularly significant for applications in action recognition, Human-Computer Interaction, and Human-Robot Interaction, where reliable hand tracking is essential. Although his citation count is still growing—with his leading paper accumulating 2 citations—Tran’s work addresses a critical challenge in enabling more natural and intuitive interfaces between humans and machines. His research stands out for its practical approach to enhancing detection under varying lighting, occlusions, and backgrounds. As the demand for seamless interaction technologies increases, Tran’s contributions are poised to have a lasting impact on the development of responsive, vision-based systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Robust Hand Detection Based on Convolutional Neural Network and Attention Module

   2 citations · 2022

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