Dense Affinity Matching for Few-Shot Segmentation

• URL: http://arxiv.org/abs/2307.08434v1
• Date: Mon, 17 Jul 2023 12:27:15 GMT
• Title: Dense Affinity Matching for Few-Shot Segmentation
• Authors: Hao Chen and Yonghan Dong and Zheming Lu and Yunlong Yu and Yingming Li and Jungong Han and Zhongfei Zhang
• Abstract summary: Few-Shot (FSS) aims to segment the novel class images with a few samples. We propose a dense affinity matching framework to exploit the support-query interaction. We show that our framework performs very competitively under different settings with only 0.68M parameters.
• Score: 83.65203917246745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-Shot Segmentation (FSS) aims to segment the novel class images with a few annotated samples. In this paper, we propose a dense affinity matching (DAM) framework to exploit the support-query interaction by densely capturing both the pixel-to-pixel and pixel-to-patch relations in each support-query pair with the bidirectional 3D convolutions. Different from the existing methods that remove the support background, we design a hysteretic spatial filtering module (HSFM) to filter the background-related query features and retain the foreground-related query features with the assistance of the support background, which is beneficial for eliminating interference objects in the query background. We comprehensively evaluate our DAM on ten benchmarks under cross-category, cross-dataset, and cross-domain FSS tasks. Experimental results demonstrate that DAM performs very competitively under different settings with only 0.68M parameters, especially under cross-domain FSS tasks, showing its effectiveness and efficiency.

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