TPSN: Transformer-based multi-Prototype Search Network for few-shot semantic segmentation. (October 2022)

• Record Type: Journal Article
• Authors: Wang, Wenjian; Duan, Lijuan; En, Qing; Zhang, Baochang; Liang, Fangfang
• Is Part Of: Computers & electrical engineering. Volume 103(2022)
• Publication Date: 2022-10
• Subjects: Semantic segmentation -- Few-shot learning -- Vision transformer -- Multiple prototypes; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108326
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Few-shot semantic segmentation aims to segment new objects in the image with limited annotations. Typically, in metric-based few-shot learning, the expression of categories is obtained by averaging global support object information. However, a single prototype cannot accurately describe a category. Meanwhile, simple foreground averaging operations also ignore the dependencies between objects and their surroundings. In this paper, we propose a novel Transformer-based Prototype Search Network (TPSN) for few-shot segmentation. We use the transformer encoder to integrate information between different image regions and then use the decoder to express a category in terms of multiple prototypes. The multi-prototype approach can effectively alleviate the feature fluctuation caused by limited annotation data. Moreover, we use adaptive prototype search during multi-prototype extraction instead of the ordinary averaging operation compared with the previous few-shot prototype framework. This helps the network integrate the different image regions' information and fuse object features with their dependent background information, obtaining more reasonable prototype expressions. In addition, to encourage the category's prototypes to focus on different parts and maintain consistency in high-level semantics, we use the diversity and consistency loss to constrain the multi-prototype training. Experiments show that our algorithm achieves state-of-the-art performance in few-shot segmentation on two datasets: PASCAL- 5 i and COCO- 2 0 i . Graphical abstract: Highlights: Multi-prototype gets better segmentation results compared with single-prototype. Transform annotated mask information to learnable Guide-Embedding can provide more detailed support for few-shot learning. The communication of image region feature can effectively help the model get context environment context around categories.