mlsanityData to 14 Oct 2026

A Joint Framework Towards Class-aware and Class-agnostic Alignment for Few-shot Segmentation

arXiv 2211.01310 ↗First posted 2 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face class biases due to class variants and background confusion. In this paper, we propose a joint framework that combines more valuable class-aware and class-agnostic alignment guidance to facilitate the segmentation. Specifically, we design a hybrid alignment module which establishes multi-scale query-support correspondences to mine the most relevant class-aware information for each query image from the corresponding support features. In addition, we explore utilizing base-classes knowledge to generate class-agnostic prior mask which makes a distinction between real background and foreground by highlighting all object regions, especially those of unseen classes. By jointly aggregating class-aware and class-agnostic alignment guidance, better segmentation performances are obtained on query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our proposed joint framework performs better, especially on the 1-shot setting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 44 of its 60 references that are in the map. First posted on arXiv 2 Nov 2022.

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44 of 60 references.

References · 44

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32 more
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