{"title":"SSNet: Learning Self-Similarity for Few-Shot Semantic Segmentation","authors":"Weisheng Lan, Yu Liu","doi":"10.1109/ICARM58088.2023.10218769","DOIUrl":null,"url":null,"abstract":"Few-shot Segmentation(FSS) refers to the task of segmenting newly introduced classes using only a limited number of closely marked examples. In the past, prototype learning based on metric and segmentation based on visual correspondence mostly ignore the matching relationship of query image itself. In this article, we present a novel self-similarity based hyperrelation network (SSNet), which introduces self-similarity generation module (SGM) and self-similarity mask module (SMM) to capture self-similarity information of target classes in query images and help the network better understand the internal similarity of query images. We also replace the feature mask with the input mask to eliminate the interference of background information in the support image. Experiments on the PASCAL-Sishow that SSNet achieves new state-of-the-art (SOTA), with a mIoU score of 64.6% in the 1-shot scenario and 68.1% in the 5-shot scenario, which is 0.62% higher than the SOTA method in the 1-shot scenario. This demonstrates that SSNet can achieve efficient and accurate few-shot segmentation with only a small number of samples.","PeriodicalId":220013,"journal":{"name":"2023 International Conference on Advanced Robotics and Mechatronics (ICARM)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Advanced Robotics and Mechatronics (ICARM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARM58088.2023.10218769","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Few-shot Segmentation(FSS) refers to the task of segmenting newly introduced classes using only a limited number of closely marked examples. In the past, prototype learning based on metric and segmentation based on visual correspondence mostly ignore the matching relationship of query image itself. In this article, we present a novel self-similarity based hyperrelation network (SSNet), which introduces self-similarity generation module (SGM) and self-similarity mask module (SMM) to capture self-similarity information of target classes in query images and help the network better understand the internal similarity of query images. We also replace the feature mask with the input mask to eliminate the interference of background information in the support image. Experiments on the PASCAL-Sishow that SSNet achieves new state-of-the-art (SOTA), with a mIoU score of 64.6% in the 1-shot scenario and 68.1% in the 5-shot scenario, which is 0.62% higher than the SOTA method in the 1-shot scenario. This demonstrates that SSNet can achieve efficient and accurate few-shot segmentation with only a small number of samples.