The Missing Link: Finding Label Relations across Datasets

摘要

Computer Vision is driven by the many datasets which can be used for training or evaluating novel methods. Each of these dataset, however, has its own design principles resulting in a different set of labels,different appearance domains and different annotation instructions. In this paper we explore the automatic discovery of visual-semantic relations between labels across datasets. We want to understand how the instances with label a in dataset A relate to the instances with label b in dataset B,are they in an identity, parent/child, or overlap relation? Or is there no visual link between these two? To find relations between labels across datasets,we propose methods based on language, on vision, and on a combination of both. In order to evaluate these we establish ground-truth relations between three datasets: COCO, ADE20k, and Berkeley Deep Drive. Our methods can effectively discover label relations across datasets and the type of the relations. We use these results for a deeper inspection on why instances relate, find missing aspects, and use our relations to create finer-grained annotations. We conclude that label relations cannot be established by looking at the label-name semantics alone, the relations depend highly on how each of the individual datasets was constructed.