Qingwei Sun, Jiangang Chao, Wanhong Lin, Wei Chen, Zhenying Xu, Jin Yang
{"title":"Few-shot segmentation combined with domain adaptation: a flexible paradigm for parsing astronaut work environments","authors":"Qingwei Sun, Jiangang Chao, Wanhong Lin, Wei Chen, Zhenying Xu, Jin Yang","doi":"10.1007/s10489-025-06508-z","DOIUrl":null,"url":null,"abstract":"<div><p>The capacity to perform few-shot segmentation of the astronaut work environment (AWE) is of critical importance, especially for tasks that cannot be predetermined. The challenging task of transferring FSS models, which are trained on natural datasets, to the AWE—referred to as cross-domain few-shot segmentation (CD-FSS)—holds substantial importance. Rather than devising an entirely novel model, we propose an approach that integrate domain adaptation (DA) with extant FSS models, herein termed meta learners. Specifically, a prior learner based on generative adversarial networks (GAN) is devised to impart semantic guidance to the meta learner. To discern challenging samples, a loss function incorporating a scaling factor is employed during the training stage of the prior learner. Furthermore, a metric-based fusion module is proposed to mitigate bias in accordance with the association between the prior learner and the meta learner. The results evince that our method can be seamlessly integrated with different types of existing FSS models, thereby enhancing their cross-domain performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Intelligence","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s10489-025-06508-z","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The capacity to perform few-shot segmentation of the astronaut work environment (AWE) is of critical importance, especially for tasks that cannot be predetermined. The challenging task of transferring FSS models, which are trained on natural datasets, to the AWE—referred to as cross-domain few-shot segmentation (CD-FSS)—holds substantial importance. Rather than devising an entirely novel model, we propose an approach that integrate domain adaptation (DA) with extant FSS models, herein termed meta learners. Specifically, a prior learner based on generative adversarial networks (GAN) is devised to impart semantic guidance to the meta learner. To discern challenging samples, a loss function incorporating a scaling factor is employed during the training stage of the prior learner. Furthermore, a metric-based fusion module is proposed to mitigate bias in accordance with the association between the prior learner and the meta learner. The results evince that our method can be seamlessly integrated with different types of existing FSS models, thereby enhancing their cross-domain performance.
期刊介绍:
With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance.
The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.