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IEEE/CAA Journal of Automatica Sinica

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Y. Liu, B. Jiang, and J. M. Xu, “Axial assembled correspondence network for few-shot semantic segmentation,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 0, pp. 1–11, Aug. 2022. 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. 9, no. 0, pp. 1–11, Aug. 2022. doi: 10.1109/JAS.2022.105863

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 in part by the Guangdong Basic and Applied Basic Research Foundation (2020B1515120071)
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  • 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.

     

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