Dat Huynh

During my PhD, I was mostly interested in how deep learning model can effectively generalizes their knowledge toward unseen concepts during training which leads me towards studying and designing novel attention mechanism for zero-shot learning. Here is a few of my works that I still enjoy reading from time to time.

[Project] [Supplementary Materials] [Slide]	D. Huynh and Adobe Research {J. Kuen and Z. Lin and J. Gu} and E. Elhamifar Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling CVPR 2022 Description: Developed a pseudo labeling framework that can generate pseudo masks for novel objects without any segmentation annotations Outcome: Improved the state-of-the-art performance of instance segmentation by 4.5% on MS-COCO and 5.1% on the large-scale Open Images & Conceptual Captions datasets
[Project] [Supplementary Materials] [Slide]	D. Huynh and E. Elhamifar Interaction Compass: Multi-Label Zero-Shot Learning of Human-Object Interactions via Spatial Relations ICCV 2021 Description: Developed a compositional model to recognize unseen human interactions based on spatial relations between human and objects Outcome: Improved the state-of-the-art performance of unseen human-object interaction recognition by 2.6% mAP on HICO dataset
[Project] [Supplementary Materials] [Slide]	D. Huynh and E. Elhamifar Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition NeurIPS 2020 Description: Developed a generative model that constructs fine-grained features for unseen classes by recombining features from training samples Outcome: Improved the state-of-the-art performance of unseen clothing recognition by 4% harmonic mean on DeepFashion dataset
[Project]	E. Elhamifar and D. Huynh Self-Supervised Multi-Task Procedure Learning from Instructional Videos ECCV 2020 Description: Developed a weakly supervised key-frame localization method for multi-task procedure learning in videos Outcome: Applied self-supervised learning on CrossTask and ProceL datasets to localize key-frames without human supervision
	S. Jafar-Zanjani, M. M. Salary, D. Huynh, E. Elhamifar, and H. Mosallaei Active Metasurfaces Design by Conditional Generative Adversarial Networks International Conference on Metamaterials, Photonic Crystals and Plasmonics, 2020
[Project] [Supplementary Materials] [Slide]	D. Huynh and E. Elhamifar A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning CVPR 2020 Oral Presentation Description: Developed a multi-label recognition system for labels without training samples via attention sharing Outcome: Improved the state-of-the-art performance by 2% mAP score on NUS-WIDE and scaled to 7000 seen labels and 400 unseen labels in Open Images
[Project] [Supplementary Materials] [Slide]	D. Huynh and E. Elhamifar Fine-Grained Generalized Zero-Shot Learning via Dense Attribute-Based Attention CVPR 2020 Description: Developed a dense attribute-based attention mechanism for fine-grained zero-shot learning Outcome: Improved state-of-the-art performances on CUB, AWA2 by at least 4% harmonic mean by weakly localizing fine-grained attributes of all classes
[Project] [Supplementary Materials] [Slide]	D. Huynh and E. Elhamifar Interactive Multi-Label CNN Learning with Partial Labels CVPR 2020 Description: Developed a scalable graph-based framework to regularize multi-label CNN learning with missing labels Outcome: Improved 2% mAP score on Open Images compared to treating missing labels as absent labels
	D. Huynh and E. Elhamifar Seeing Many Unseen Labels via Shared Multi-Attention Models ICCVW 2019 Workshop on Multi-Discipline Approach for Learning Concepts - Zero-Shot, One-Shot, Few-Shot and Beyond