论文标题
多域语义分段的自动通用分类学
Automatic universal taxonomies for multi-domain semantic segmentation
论文作者
论文摘要
在多个数据集上的培训语义细分模型引起了对计算机视觉社区的最新兴趣。这种兴趣是由昂贵的注释和渴望在多个视觉领域熟练的愿望引起的。但是，已建立的数据集具有相互不相容的标签，这些标签破坏了野生中原则上的推断。我们通过迭代数据集集成自动构建通用分类法来解决这个问题。我们的方法检测数据集特异性标签之间的子集 - 苏布尔关系，并通过将超级类作为部分标签来支持子类liogits的学习。我们介绍了有关标准数据集收集的实验，并在以前的工作中展示了竞争性的概括性能。
Training semantic segmentation models on multiple datasets has sparked a lot of recent interest in the computer vision community. This interest has been motivated by expensive annotations and a desire to achieve proficiency across multiple visual domains. However, established datasets have mutually incompatible labels which disrupt principled inference in the wild. We address this issue by automatic construction of universal taxonomies through iterative dataset integration. Our method detects subset-superset relationships between dataset-specific labels, and supports learning of sub-class logits by treating super-classes as partial labels. We present experiments on collections of standard datasets and demonstrate competitive generalization performance with respect to previous work.
