TY - GEN
T1 - PROTOTYPE QUEUE LEARNING FOR MULTI-CLASS FEW-SHOT SEMANTIC SEGMENTATION
AU - Wang, Zichao
AU - Jiang, Zhiyu
AU - Yuan, Yuan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Few-shot semantic segmentation aims to undertake the segmentation task of novel classes with only a few annotated images. However, most existing methods tend to segment the foreground and background in the image, which limits practical application. In this paper, we present a Prototype Queue Network, which performs few-shot segmentation on multi-class in the images by aggregating binary classes into multiple classes. A prototype queue learning module is proposed to achieve multi-class segmentation by mining the relationship among features of different classes with queue and pseudo labels. In addition, a background latent class distribution refinement module is proposed to prevent the latent novel class in the background from being incorrectly predicted, which refines the boundary among different classes. Furthermore, we propose a two-steps segmentation module to optimize the process of extracting feature representation by adding progressive constraints, which can further improve the accuracy of segmentation. Experiments on the UDD and Vaihingen datasets demonstrate that our method achieves state-of-the-art performance.
AB - Few-shot semantic segmentation aims to undertake the segmentation task of novel classes with only a few annotated images. However, most existing methods tend to segment the foreground and background in the image, which limits practical application. In this paper, we present a Prototype Queue Network, which performs few-shot segmentation on multi-class in the images by aggregating binary classes into multiple classes. A prototype queue learning module is proposed to achieve multi-class segmentation by mining the relationship among features of different classes with queue and pseudo labels. In addition, a background latent class distribution refinement module is proposed to prevent the latent novel class in the background from being incorrectly predicted, which refines the boundary among different classes. Furthermore, we propose a two-steps segmentation module to optimize the process of extracting feature representation by adding progressive constraints, which can further improve the accuracy of segmentation. Experiments on the UDD and Vaihingen datasets demonstrate that our method achieves state-of-the-art performance.
KW - Few-shot segmentation
KW - Multi-class segmentation
KW - Prototype learning
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85146637686&partnerID=8YFLogxK
U2 - 10.1109/ICIP46576.2022.9897698
DO - 10.1109/ICIP46576.2022.9897698
M3 - 会议稿件
AN - SCOPUS:85146637686
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1721
EP - 1725
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -