{"title":"用于少量语义分割的循环关联原型网络","authors":"","doi":"10.1016/j.engappai.2024.109309","DOIUrl":null,"url":null,"abstract":"<div><p>Few-shot segmentation aims to train a segmentation model that can quickly adapt to novel classes referring to only a few annotated samples. Existing few-shot segmentation methods are based on the meta-learning strategy and extract support samples’ information from a support set and then apply the information to make predictions on query images. However, most methods abstract support features into prototype vectors and ignore the crucial relationship between query and support samples. To address the problem, we propose a cycle association prototype network that focuses on pixel-wise relationships between support and query images for more accurate segmentation. Specifically, a cycle-consistent prototype module is proposed to select reliable support features and to generate prototype. To capture cross-scale relations and overcome object variations, we introduce a scale-aware prior mask generation module to offer rich guidance for objects of varying sizes and shapes via calculating the pixel-level similarity between the support and query image features. Finally, a mask generation module, which contains two parallel modules, feature fusion module and transformer decoder, is utilized to predict the query image. Extensive experiments on two datasets show that our method yields superior performance with state-of-the-art methods.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cycle association prototype network for few-shot semantic segmentation\",\"authors\":\"\",\"doi\":\"10.1016/j.engappai.2024.109309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Few-shot segmentation aims to train a segmentation model that can quickly adapt to novel classes referring to only a few annotated samples. Existing few-shot segmentation methods are based on the meta-learning strategy and extract support samples’ information from a support set and then apply the information to make predictions on query images. However, most methods abstract support features into prototype vectors and ignore the crucial relationship between query and support samples. To address the problem, we propose a cycle association prototype network that focuses on pixel-wise relationships between support and query images for more accurate segmentation. Specifically, a cycle-consistent prototype module is proposed to select reliable support features and to generate prototype. To capture cross-scale relations and overcome object variations, we introduce a scale-aware prior mask generation module to offer rich guidance for objects of varying sizes and shapes via calculating the pixel-level similarity between the support and query image features. Finally, a mask generation module, which contains two parallel modules, feature fusion module and transformer decoder, is utilized to predict the query image. Extensive experiments on two datasets show that our method yields superior performance with state-of-the-art methods.</p></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624014672\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624014672","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Cycle association prototype network for few-shot semantic segmentation
Few-shot segmentation aims to train a segmentation model that can quickly adapt to novel classes referring to only a few annotated samples. Existing few-shot segmentation methods are based on the meta-learning strategy and extract support samples’ information from a support set and then apply the information to make predictions on query images. However, most methods abstract support features into prototype vectors and ignore the crucial relationship between query and support samples. To address the problem, we propose a cycle association prototype network that focuses on pixel-wise relationships between support and query images for more accurate segmentation. Specifically, a cycle-consistent prototype module is proposed to select reliable support features and to generate prototype. To capture cross-scale relations and overcome object variations, we introduce a scale-aware prior mask generation module to offer rich guidance for objects of varying sizes and shapes via calculating the pixel-level similarity between the support and query image features. Finally, a mask generation module, which contains two parallel modules, feature fusion module and transformer decoder, is utilized to predict the query image. Extensive experiments on two datasets show that our method yields superior performance with state-of-the-art methods.
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.