Background matting via recursive excitation
Publication Type
Conference Proceeding Article
Publication Date
6-2022
Abstract
We propose a simple yet effective technique that significantly improves the performance of the current state-of-the-art background matting model without compromising its original speed. We achieve this by carefully exciting the proper neural activations using an excitation map in the training phase and performing recursive inference in the testing phase. To avoid being over-reliant on perfect excitations, we follow the idea of curriculum learning to divide the training phase into three easy-to-hard stages and gradually shift the excitation map from GT alpha matte to pseudo GT alpha matte. In the testing phase, we propose a recursive inference mechanism that uses the output alpha matte as the excitation map to further refine the output alpha matte. Our method is a simple plug-in for arbitrary matting models. Compared with the original ones, the enhanced models alleviate the problem of performance degradation with complex background and thus boosts the matting accuracy.
Keywords
image matting, recursive excitation, inference mechanisms, degradation
Discipline
Computer Sciences | Graphics and Human Computer Interfaces
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE International Conference on Multimedia and Expo (ICME)
Identifier
10.1109/ICME52920.2022.9859876
Publisher
IEEE
City or Country
Taipei
Citation
DENG, Junjie.; XU, Yangyang.; HE, Shengfeng.; and HE, Shengfeng.
Background matting via recursive excitation. (2022). IEEE International Conference on Multimedia and Expo (ICME).
Available at: https://ink.library.smu.edu.sg/sis_research/8557