OSM: An Open Set Matting Framework with OOD Detection and Few-Shot Learning
Yuhongze Zhou (McGill University),* Issam Hadj Laradji (ServiceNow), Liguang Zhou (The Chinese University of Hong Kong, Shenzhen), Derek Nowrouzezahrai (McGill University)
The 33rd British Machine Vision Conference
Abstract
Natural image matting is the task of precisely estimating alpha mattes to separate foreground objects from background images. Existing matting methods only focus on classical closed-set problems where object categories and data distributions are similar between training and test sets. However, in the open world setup, there exists a situation where testing samples are drawn from a different distribution than the training data. To handle this situation, we present the first open set matting (OSM) framework that contains two networks: (1) an out-of-distribution (OOD) detection network to identify OOD to-be-matted objects; and (2) an incremental few-shot learning matting module to enlarge the existing knowledge base of to-be-matted objects. Our OOD detection network leverages metric-based prototype learning to be aware of unseen objects and increase inter-class separability, utilizing intra-batch connections to enhance intra-class compactness. Compared to other OOD detection methods, our network achieves state-of-the-art performance on SIMD dataset. Further, our incremental few-shot learning matting module improves the performance on unseen to-be-matted objects by gradually incorporating novel classes into the existing knowledge base without catastrophic forgetting and over-fitting.Video
Citation
@inproceedings{Zhou_2022_BMVC,
author = {Yuhongze Zhou and Issam Hadj Laradji and Liguang Zhou and Derek Nowrouzezahrai},
title = {OSM: An Open Set Matting Framework with OOD Detection and Few-Shot Learning},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0092.pdf}
}
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