Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2021
Abstract
This paper represents object instance as a terrace, where the height of terrace corresponds to object attention while the evolution of layers from peak to sea level represents the complexity in drawing the finer boundary of an object. A multitask neural network is presented to learn the terrace representation. The attention of terrace is leveraged for instance counting, and the layers provide prior for easy-to-hard pathway of progressive instance segmentation. We study the model for counting and segmentation for a variety of food instances, ranging from Chinese, Japanese to Western food. This paper presents how the terrace model deals with arbitrary shape, size, obscure boundary and occlusion of instances, where other techniques are currently short of.
Keywords
Segmentation, Object Detection & Categorization, Applications
Discipline
Artificial Intelligence and Robotics | Software Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Conference, 2021 February 2-9
Volume
35
First Page
2364
Last Page
2372
Publisher
AAAI
City or Country
USA
Citation
NGUYEN, Huu-Thanh and NGO, Chong-wah.
Terrace-based food counting and segmentation. (2021). Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual Conference, 2021 February 2-9. 35, 2364-2372.
Available at: https://ink.library.smu.edu.sg/sis_research/6218
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.