Welcome to Hui Wu's website


Research scientist and manager. Distributed AI, IBM Research.

Welcome to Hui Wu's website


Profile Photo

About Hui Wu

I am a research scientist and a research manager at the Distributed AI group, IBM Research. I am passionate about developing new AI applications that drive real-world impact, particulary around Edge Data and AI use cases. My current research interest includes vision and language, neural-symbolic learning, computer vision for fashion and more recently, distributed AI. I joined IBM Research since 2015, and worked in the computer vision and multimedia group at IBM Research AI (2015-2020), and MIT-IBM Watson AI Lab (2020-2021). Prior to joining IBM, I received my PhD in Computer Science from UNC Charlotte, with a thesis focus on machine learning techniques applied to image set analysis problems.

Contact me: wuhu AT us.ibm.com

Updates

Selected Projects

Dialog-based Interactive Image Retrieval

Dialog-based Interactive Image Retrieval
Xiaoxiao Guo*, Hui Wu*, Yu Cheng, Steven J. Rennie, Gerald Tesauro and Rogério S. Feris (* equal contribution)
NeurIPS 2018 [PDF] [CODE] [DEMO]

Overview

We proposed a novel type of dialog agent for the task of interactive image retrieval. Recently, there has been a rapid rise of research interest in visually grounded conversational agents, driven by the progress of deep learning techniques for both image and natural language understanding. A few interesting application scenarios have been explored by recent work, such as collaborative drawing, visual dialog and object guessing game. In this work, we tested the value of visually grounded dialog agents in a practical and yet challenging context. Specifially, we proposed a novel framework of image retrieval system which learns to seek natural and expressive dialog feedbacks from the user and iteratively refine the retrieval result.

Pooling with Stochastic Spatial Sampling

S3pool - Pooling with stochastic spatial sampling
Zhai, Shuangfei, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, and Rogerio Feris
CVPR 2017 [PDF] [CODE]

Semantic-aware Food Visual Recognition

Learning to make better mistakes - Semantics-aware visual food recognition
Hui Wu, Michele Merler, Rosario Uceda-Sosa and John R. Smith
ACM Multimedia, 2016 [PDF] [Watson API]

The growing popularity of fitness applications and people’s need for easy logging of calorie consumption on mobile devices has made accurate food visual recognition increasingly desireable. In this project, we proposed a visual food recognition framework that integrates the semantic relationships among fine-grained food classes.