18 citations · 73 across the 41 of their papers we have counts for
7 papers · 1 filter
Online Continual Learning Via Candidates Voting
Jiangpeng He, Fengqing Zhu
Continual learning in online scenario aims to learn a sequence of new tasks from data stream using each data only once for training, which is more realistic than in offline mode as…
An Integrated System for Mobile Image-Based Dietary Assessment
Zeman Shao, Yue Han, Jiangpeng He +5
Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as…
Improving Dietary Assessment Via Integrated Hierarchy Food Classification
Runyu Mao, Jiangpeng He, Luotao Lin +3
Image-based dietary assessment refers to the process of determining what someone eats and how much energy and nutrients are consumed from visual data. Food classification is the fi…
Online Continual Learning For Visual Food Classification
Jiangpeng He, Fengqing Zhu
Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially a…
Unsupervised Continual Learning Via Pseudo Labels
Jiangpeng He, Fengqing Zhu
Continual learning aims to learn new tasks incrementally using less computation and memory resources instead of retraining the model from scratch whenever new task arrives. However…
Towards Learning Food Portion From Monocular Images With Cross-Domain Feature Adaptation
Zeman Shao, Shaobo Fang, Runyu Mao +5
We aim to estimate food portion size, a property that is strongly related to the presence of food object in 3D space, from single monocular images under real life setting. Specific…
An End-to-End Food Image Analysis System
Jiangpeng He, Runyu Mao, Zeman Shao +4
Modern deep learning techniques have enabled advances in image-based dietary assessment such as food recognition and food portion size estimation. Valuable information on the types…