论文标题
群集到适应：几乎没有射击域的适应性，用于跨不同标签的语义分割
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic Segmentation across Disjoint Labels
论文作者
论文摘要
在相同类别组成的跨数据集的语义分割的域适应性已经取得了一些成功。但是，更一般的情况是源和目标数据集对应于非重叠标签空间时。例如，分段数据集中的类别根据环境或应用程序的类型发生了很大变化，但共享许多有价值的语义关系。基于特征一致性或差异最小化的现有方法不会考虑这种类别的转移。在这项工作中，我们提出了群集到适应（C2A），这是一种基于计算有效的聚类方法，用于跨分割数据集的域适应性，这些方法具有完全不同但可能相关的类别。我们表明，在变换的特征空间中强制执行的这种聚类目标可以自动选择跨源和目标域的类别，这些类别可以对齐以改善目标性能，同时防止对无关类别的负转移。我们通过实验在室内适应性的挑战性问题上进行了较少的室内适应性问题，以少量拍摄以及零拍设置来证明我们的方法的有效性，在所有情况下，性能对现有方法和基线的绩效持续改善。
Domain adaptation for semantic segmentation across datasets consisting of the same categories has seen several recent successes. However, a more general scenario is when the source and target datasets correspond to non-overlapping label spaces. For example, categories in segmentation datasets change vastly depending on the type of environment or application, yet share many valuable semantic relations. Existing approaches based on feature alignment or discrepancy minimization do not take such category shift into account. In this work, we present Cluster-to-Adapt (C2A), a computationally efficient clustering-based approach for domain adaptation across segmentation datasets with completely different, but possibly related categories. We show that such a clustering objective enforced in a transformed feature space serves to automatically select categories across source and target domains that can be aligned for improving the target performance, while preventing negative transfer for unrelated categories. We demonstrate the effectiveness of our approach through experiments on the challenging problem of outdoor to indoor adaptation for semantic segmentation in few-shot as well as zero-shot settings, with consistent improvements in performance over existing approaches and baselines in all cases.
