Minh Quan Tran
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Postdoc
Also affiliated: Vietnam National University Ho Chi Minh City (2020–2025); Johns Hopkins University (2019–2021); Ho Chi Minh City University of Science (2020–2025); Johns Hopkins University Applied Physics Laboratory (2019–2025); Can Tho Central General Hospital (2024); University of Maryland, Baltimore County (2026)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Minh Quan Tran's research centers on the application of advanced algorithms and machine learning techniques to diverse data analysis challenges. His work spans areas including medical image processing, with publications focusing on automatic sleep staging using multi-view sequential models and long sequence modeling. He has also investigated deep federated learning for autonomous driving and explored meta-model quantification for medical visual question answering.
Tran's research portfolio also includes work on aerial image segmentation utilizing multi-resolution transformers and the development of toolkits for facial expression analysis. He has explored light-weight deformable registration using adversarial learning and has published on pre-trained audio-visual transformers for emotion recognition. His scholarship metrics include an h-index of 11, with over 1,100 citations across 69 publications.
Metrics
- h-index: 7
- Publications: 23
- Citations: 377
Positions
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Postdoc publications 2024–2026University of Arkansas at Fayetteville Listing
Selected Publications
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DualFit: A Two-Stage Virtual Try-On via Warping and Synthesis (2025)
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Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking (2025)
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Miga: Multi-Chicken Gait Assessment (2025)
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A2VIS: Amodal-Aware Approach to Video Instance Segmentation (2025)
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S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling (2025)
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AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation (2024)
Collaboration Network
Top Collaborators
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Less Labeled Data
- CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect
- CapsNet for medical image segmentation
- DNA: Deformable Neural Articulations Network for Template-free Dynamic 3D Human Reconstruction from Monocular RGB-D Video
Showing 5 of 8 shared publications
- CapsNet for medical image segmentation
- DNA: Deformable Neural Articulations Network for Template-free Dynamic 3D Human Reconstruction from Monocular RGB-D Video
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- DNA: Deformable Neural Articulations Network for Template-free Dynamic 3D Human Reconstruction from Monocular RGB-D Video
- CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect
- Miga: Multi-Chicken Gait Assessment
- CarcassFormer: an end-to-end transformer-based framework for simultaneous localization, segmentation and classification of poultry carcass defect
- Miga: Multi-Chicken Gait Assessment
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- AerialFormer: Multi-Resolution Transformer for Aerial Image Segmentation
- Land8Fire: A Complete Study on Wildfire Segmentation Through Comprehensive Review, Human-Annotated Multispectral Dataset, and Extensive Benchmarking
- SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Less Labeled Data
- SS-3DCAPSNET: Self-Supervised 3d Capsule Networks for Medical Segmentation on Less Labeled Data
- DNA: Deformable Neural Articulations Network for Template-free Dynamic 3D Human Reconstruction from Monocular RGB-D Video
- CapsNet for medical image segmentation
- CapsNet for medical image segmentation
- CapsNet for medical image segmentation
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