Welcome to Hui Wu's website

Research Scientist, IBM Research AI

Welcome to Hui Wu's website

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.

Image Manifold Learning

Robust regression on image manifolds for ordered label denoising
Hui Wu and Richard Souvenir
CVPR 2015 [PDF] [CODE]

Image manifolds offer a perceptually meaningful model to organize images for certain types of natural image sets. However, unsupervised methods are limited in the situations where a discriminant factor needs to be recovered from an image set with multiple latent factors of variation. Meanwhile, supervised manifold learning approaches incorporate image labels that provide additional constraints to the relationships between images and can be robust against irrelevant factors. During the course of my PhD study, I was very interested in learning on image manifolds with weak supervision, which needs much less manual labeling effort than supervised methods. My work considered three variants of weakly supervised learning on image manifolds, when image labels do not explain all the latent factors of image variation, or are only partly available or highly corrupted.