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<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>

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Few-shot segmentation combined with domain adaptation: a flexible paradigm for parsing astronaut work environments

  • 期刊论文

  • 发表日期：

    2025年4月14日

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  • Qingwei Sun,
  • Jiangang Chao,
  • Wanhong Lin,
  • Wei Chen,
  • Zhenying Xu,
  • Jin Yang

摘要

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.