Improving Model Adaptation for Semantic Segmentation by Learning Model-Invariant Features with Multiple Source-Domain Models
Improving Model Adaptation for Semantic Segmentation by Learning Model-Invariant Features with Multiple Source-Domain Models
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DOI：
10.1109/icip46576.2022.9897487
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Zongyao Li;Ren Togo;Takahiro Ogawa;M. Haseyama
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文献类型：
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作者：
Zongyao Li;Ren Togo;Takahiro Ogawa;M. Haseyama
在本文中，我们重点研究了一个有待研究的问题：多源模型自适应，它是在多源无监督领域自适应的基础上，用源域预先训练的模型来代替源域数据。预先训练的模型总是比训练数据更容易共享，因此在许多实际场景中都可以使用多个源域模型。因此，多源域自适应的问题设置在实际应用中具有一定的实用价值。在这种背景下，我们挑战了语义分割的任务，这一任务在传统的无监督领域自适应中也是困难的，因为像素级别的知识转移。该方法通过学习模型不变特征，充分利用多个源域模型的优势，从不同源域预先训练的模型中获取具有相似分布的目标域特征。利用模型不变特征学习训练的自适应模型得益于源域模型的多样性，从而可以产生更具泛化能力的特征到目标域。在几种自适应环境下的实验结果验证了该方法的有效性和优越性。
In this paper, we focus on a problem remaining to be studied: multi-source model adaptation, which is derived from multi-source unsupervised domain adaptation and replaces the source-domain data with source-domain pre-trained models. Pre-trained models are always easier to share than training data so that multiple source-domain models are available in many practical scenarios. Therefore, the problem setting of multi-source domain adaptation is practical in real-world applications. In this setting, we challenge the task of semantic segmentation which is difficult also in the traditional unsupervised domain adaptation due to the pixel-level knowledge transfer. Our method takes full advantage of the multiple source-domain models by learning model-invariant features, which aims to obtain target-domain features with similar distributions from the models pre-trained in different source domains. The adaptation models trained with the model-invariant feature learning benefit from the diversity of the source-domain models and can thus produce more generalizable features to the target domain. Experimental results in several adaptation settings validate the effectiveness and superiority of our method.