MFNet: Multi-class Few-shot Segmentation Network with Pixel-wise Metric Learning

Miao Zhang, Miaojing Shi, Li Li

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In visual recognition tasks, few-shot learning requires the ability to learn object categories with few support examples. Its re-popularity in light of the deep learning development is mainly in image classification. This work focuses on few-shot semantic segmentation, which is still a largely unexplored field. A few recent advances are often restricted to single-class few-shot segmentation. In this paper, we first present a novel multi-way (class) encoding and decoding architecture which effectively fuses multi-scale query information and multi-class support information into one query-support embedding. Multi-class segmentation is directly decoded upon this embedding. For better feature fusion, a multi-level attention mechanism is proposed within the architecture, which includes the attention for support feature modulation and attention for multi-scale combination. Last, to enhance the embedding space learning, an
additional pixel-wise metric learning module is introduced with triplet loss formulated on the pixel-level of the query-support embedding. Extensive experiments on standard benchmarks PASCAL-5i and COCO-20i show clear benefits of our method over the state of the art in multi-class few-shot segmentation.
Original languageEnglish
Publication statusAccepted/In press - 14 Jul 2022


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