Few-shot Segmentation with Optimal Transport Matching and Message Flow
- URL: http://arxiv.org/abs/2108.08518v1
- Date: Thu, 19 Aug 2021 06:26:11 GMT
- Title: Few-shot Segmentation with Optimal Transport Matching and Message Flow
- Authors: Weide Liu, Chi Zhang, Henghui Ding, Tzu-Yi Hung and Guosheng Lin
- Abstract summary: It is essential for few-shot semantic segmentation to fully utilize the support information.
We propose a Correspondence Matching Network (CMNet) with an Optimal Transport Matching module.
Experiments on PASCAL VOC 2012, MS COCO, and FSS-1000 datasets show that our network achieves new state-of-the-art few-shot segmentation performance.
- Score: 50.9853556696858
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We address the challenging task of few-shot segmentation in this work. It is
essential for few-shot semantic segmentation to fully utilize the support
information. Previous methods typically adapt masked average pooling over the
support feature to extract the support clues as a global vector, usually
dominated by the salient part and loses some important clues. In this work, we
argue that every support pixel's information is desired to be transferred to
all query pixels and propose a Correspondence Matching Network (CMNet) with an
Optimal Transport Matching module to mine out the correspondence between the
query and support images. Besides, it is important to fully utilize both local
and global information from the annotated support images. To this end, we
propose a Message Flow module to propagate the message along the inner-flow
within the same image and cross-flow between support and query images, which
greatly help enhance the local feature representations. We further address the
few-shot segmentation as a multi-task learning problem to alleviate the domain
gap issue between different datasets. Experiments on PASCAL VOC 2012, MS COCO,
and FSS-1000 datasets show that our network achieves new state-of-the-art
few-shot segmentation performance.
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