论文标题
联合设定的多源模型改编，用于语义细分
Union-set Multi-source Model Adaptation for Semantic Segmentation
论文作者
论文摘要
本文解决了用于语义分割的多源模型改编问题的广义版本。提出了模型适应为新的域适应性问题，该问题需要访问预训练的模型，而不是用于源域的数据。模型适应性的一般多源设置严格假定每个源域都与目标域共享一个共同的标签空间。作为放松，我们允许每个源域的标签空间作为目标域的子集，并要求源域标签空间的结合等于目标域标签空间。对于名为Union-Set Multi-Source模型适应的新设置，我们提出了一种具有新型学习策略的方法，名为Model-Invariant特征学习，该方法充分利用了源域模型的各种特征，从而改善了目标域中的概括。我们在各种适应设置中进行了广泛的实验，以显示我们方法的优越性。该代码可从https://github.com/lzy7976/union-set-model-apaptation获得。
This paper solves a generalized version of the problem of multi-source model adaptation for semantic segmentation. Model adaptation is proposed as a new domain adaptation problem which requires access to a pre-trained model instead of data for the source domain. A general multi-source setting of model adaptation assumes strictly that each source domain shares a common label space with the target domain. As a relaxation, we allow the label space of each source domain to be a subset of that of the target domain and require the union of the source-domain label spaces to be equal to the target-domain label space. For the new setting named union-set multi-source model adaptation, we propose a method with a novel learning strategy named model-invariant feature learning, which takes full advantage of the diverse characteristics of the source-domain models, thereby improving the generalization in the target domain. We conduct extensive experiments in various adaptation settings to show the superiority of our method. The code is available at https://github.com/lzy7976/union-set-model-adaptation.
