• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    September 28, 2021: Mrs. Nguyen Thi Hong Nhung (Univ. Transport & Communications), Two-stage Convolutional Neural Networks for Road Crack Detection and Segmentation

    Automatic detection of road cracks is an important task to support road inspection for transport infrastructure. Various methods have been proposed for road crack detection and segmentation, however, there is no established method for handling real road images that are noisy and of low quality. Inthis talk, I will present our proposed method utilizing a twostage convolutional neural network (CNN) for road crackdetection and segmentation in images at the pixel level. Extensive experiments on several datasets, including public sources and our collected dataset, have been conducted.The experimental results show that the tbeen proposed for road crack detection and

    Speaker: Mrs. Nguyen Thi Hong Nhung, Univ. Transport & Communications

    Time: 15:30, Tuesday, September 28, 2021

    Venue: Webinar; Access code: https://bit.ly/3kEiuGM


    Nhung H.T. Nguyen received the B.S. degree and the M.S. degree in Electronics and Telecommunications from Hanoi University of Technology and Science, Hanoi, Vietnam, in 2008 and 2010, respectively. She is currently pursuing the Ph.D. degree in computer science in Faculty of Engineering and Information Technology, University of Technology Sydney, Australia. She has worked as a lecturer at the University of Transport and Communications (UTC), Hanoi, Vietnam, from 2011. Her research interest includes the digital image processing, machine learning, computer science and the applications of artificial intelligent (AI) in transport. Mrs. Nguyen’s awards include a Scholarship with the University of Technology Sydney in the Ph.D. joint program with the Vietnam National University.


    February 2, 2024: Dr. Khoa D. Doan (Vin Univeristy) Toward Reliable and Practical Machine Learning Applications

    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

    February 2, 2024: Prof. Heng Ji (University of Illinois at Urbana-Champaign), Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life

    A research overview of ongoing research projects, especially focusing on two that are most related to the VinUni-UIUC Smart Health Center: (1) Misinformation Detection and Trustworthy Large Language Models; (2) Joint Natural Language and Molecule Learning for Drug Discovery. Unsurprisingly these two seemingly different research problems can be tackled with a unified approach based on […]

    January 11, 2024: Dr. Le Duc Trong (FIT-UET) Resilient Multimodal Learning for Multimodal Emotion Recognition in the Presence of Incomplete Modalities

    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]