A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 3
Mar.  2023

IEEE/CAA Journal of Automatica Sinica

• JCR Impact Factor: 7.847, Top 10% (SCI Q1)
CiteScore: 13.0, Top 5% (Q1)
Google Scholar h5-index: 64， TOP 7
Turn off MathJax
Article Contents
Y. Liu, B. Jiang, and  J. M. Xu,  “Axial assembled correspondence network for few-shot semantic segmentation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 711–721, Mar. 2023. doi: 10.1109/JAS.2022.105863
 Citation: Y. Liu, B. Jiang, and  J. M. Xu,  “Axial assembled correspondence network for few-shot semantic segmentation,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 711–721, Mar. 2023.

# Axial Assembled Correspondence Network for Few-Shot Semantic Segmentation

##### doi: 10.1109/JAS.2022.105863
Funds:  This work was supported in part by the Key Research and Development Program of Guangdong Province (2021B0101200001) and the Guangdong Basic and Applied Basic Research Foundation (2020B1515120071)
• Few-shot semantic segmentation aims at training a model that can segment novel classes in a query image with only a few densely annotated support exemplars. It remains a challenge because of large intra-class variations between the support and query images. Existing approaches utilize 4D convolutions to mine semantic correspondence between the support and query images. However, they still suffer from heavy computation, sparse correspondence, and large memory. We propose axial assembled correspondence network (AACNet) to alleviate these issues. The key point of AACNet is the proposed axial assembled 4D kernel, which constructs the basic block for semantic correspondence encoder (SCE). Furthermore, we propose the deblurring equations to provide more robust correspondence for the aforementioned SCE and design a novel fusion module to mix correspondences in a learnable manner. Experiments on PASCAL-5i reveal that our AACNet achieves a mean intersection-over-union score of 65.9   %   for 1-shot segmentation and 70.6   %   for 5-shot segmentation, surpassing the state-of-the-art method by 5.8   %   and 5.0   %   respectively.

### Catalog

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

Figures(5)  / Tables(8)

## Article Metrics

• This work achieves a mean Intersection-over-Union score of $65.9\%$ and $70.6\%$ on PASCAL-5$^i$ for 1-shot and 5-shot settings respectively, outperforming state-of-the-art results by $5.8\%$ and $5.0\%$ respectively