About Me

Hi,
I am a Ph.D. candidate at Khoury College of Computer Sciences, Northeastern University, advised by Prof. Ehsan Elhamifar.

I received my B.S. from University of Sciences (Viet Nam) where I was fortunate to study in Advanced Program in Computer Science.

Contact

If you are interested in my research or collaboration, I can be reached via:

Research Interest

My research interests lie in significantly reducing the amount of annotations needed to train deep learning systems for visual recognition, detection and segmentation tasks.
Specifically, I design methods that decompose complex concepts into primitive components that can be combined to enable learning with few or zero training samples, with missing annotations and with weak supervision.

Research Areas:

I am currently working on:

News

Publications

"We can only see a short distance ahead,
but we can see plenty there that needs to be done."
― Alan Turing, Computing machinery and intelligence, 1950 ―
[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

Services

I am always proud of serving the research community as: