Rate My Professor Toan Do

Do's research specializations lie in computer vision and machine learning, with particular emphasis on visual search, visual question answering, metric learning, compact deep learning, few-shot learning, and robotic vision applications. His scholarly impact is evidenced by over 4,690 citations and an h-index of 39 according to Google Scholar. Notable awards include the Harold Boley Award for Most Promising Paper in 2021 for 'Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification' presented at RuleML+RR 2021, and selection as a CVPR 2019 Best Paper Finalist for 'SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences'. Key publications encompass 'Task Weighting in Meta-learning with Trajectory Optimisation' (2023, Transactions on Machine Learning Research), 'Instance-level Few-shot Learning with Class Hierarchy Mining' (2023, IEEE Transactions on Image Processing), 'PAC-Bayes Meta-learning with Implicit Task-specific Posteriors' (2022, IEEE Transactions on Pattern Analysis and Machine Intelligence), 'Camouflaged Instance Segmentation In-the-Wild: Dataset, Method, and Benchmark Suite' (2021, IEEE Transactions on Image Processing), 'Big data directed acyclic graph model for real-time COVID-19 twitter stream detection' (2021, Pattern Recognition), and 'MirrorNet: Bio-Inspired Camouflaged Object Segmentation' (2021). As Chief Investigator, he leads the project 'Large-scale multimodal knowledge management: From organization and user modeling to fast contextual presentation' spanning 2022 to 2025. Do also teaches courses such as Intelligent Image and Video Analysis at Monash University.