Dat Huynh

About Me

Xin chào (Hi),
I am a Senior Research Scientist @ Meta.

I did my Ph.D. at Khoury College of Computer Sciences, Northeastern University, advised by Prof. Ehsan Elhamifar. I received my B.Sc. 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:

Email: badat0202 [at] gmail [dot] com
Linkedin

Research Interest

I am currently working on efficiency training and serving methods to effectively productionize large language models ranging from model quantization, pruning, and decoding.
My PhD background was at significantly reducing the amount of annotation needed to train visual perceptual models for large-scale recognition, detection, and segmentation. As such, I developed cross-modal embedding methods that transfer rich knowledge from human languages to the data-scarcity visual modality. These methods can effectively deal with few or even zero training samples, with missing annotations, and with weak supervision.

News

I am excited to join Meta as Research Scientist working on Large Language Models.
Our latest work, in collaboration with Adobe Research, on Open-Vocabulary Instance Segmentation is accepted at CVPR 2022.
Our work on Zero-shot Human-Object Interaction is accepted at ICCV 2021.
I am honored to receive J.P. Morgan PhD Fellowship 2021 Award.
New paper on Compositional Learning is accepted at neurIPS 2020. Code is available on Github.
New paper on Self-Supervised Multi-Task Procedure Learning from Instructional Videos is accepted at ECCV 2020. Code is available on Github.
Three papers including an oral presentation are accepted at CVPR 2020. Their codes are released on Github.

Publications

[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