Hexiang (Frank) Hu
Ph.D. Student [at] USC [at]
Deep Learner
I am passionate with Machine Learning, Computer Vision as well as Natural Language Processing. My objective is to combine the power of vision and language for robots.


Hexiang Hu is a Computer Science Ph.D. student in Viterbi School of Engineering at University of Southern California (USC), working with Prof. Fei Sha. Prior to this, He was a Ph.D. student in Henry Samueli School of Engineering and Applied Science at University of California, Los Angeles (UCLA). He earned his Bachelor’s degrees in Computer Science from Zhejiang University and Simon Fraser University with honor. He worked with Prof. Greg Mori during his undergrads. His research interests lie in the field of Machine Learning, Computer Vision and Natural Language Processing. [ Résumé ]


Spring 2019
Visitor @ Berkeley AI Research Lab
Summer 2018
Intern @ Facebook AI Research
2017 -
PhD student @ USC
Large Scale Machine Learning, Vision and Language
Supervisor: Prof. Fei Sha
2016 - 2017
PhD student @ UCLA
Deep Learning, Vision
Supervisor: Prof. Fei Sha

Selected Publications

Binary Image Selection (BISON):Interpretable Evaluation of Visual Grounding

This paper presents an alternative evaluation task for visual-grounding systems: given a caption the system is asked to select the image that best matches the caption from a pair of semantically similar images. The system's accuracy on this Binary Image SelectiON (BISON) task is not only interpretable, but also measures the ability to relate fine-grained text content in the caption to visual content in the images.

Tech Report
Synthesized Policies for Transfer and Adaptation across Tasks and Environments

In this paper, we consider the problem of learning to simultaneously transfer across both environments (ENV) and tasks (TASK), probably more importantly, by learning from only sparse (ENV, TASK) pairs out of all possible combinations. We propose a compositional neural network which depicts a meta rule for composing policies from the environment and task embeddings.

NIPS 2018 (Spotlight) Montreal, QC
Learning Structured Inference Neural Networks with Label Relations

We propose a generic structured model that leverages diverse label relations to improve image classification performance. It employs a novel stacked label prediction neural network, capturing both inter-level and intra-level label semantics. The design of this framework naurally extends to leverage partial observations in the label space to inference the rest label space.

CVPR 2016 & T-PAMI 2019
Being Negative but Constructively: Lessons Learnt from Creating Better Visual Question Answering Datasets

We show the design of the decoy answers has a significant impact on how and what the learning models learn from the datasets. In particular, the resulting learner can ignore the visual information, the question, or the both while still doing well on the task.

NAACL-HLT 2018 (Oral) in New Orleans, Louisiana
Learning Answer Embedding for Visual Question Answering

We propose a novel probabilistic model for visual question answering.

CVPR 2018 in Salt Lake City, Utah