The Missing Link: Finding Label Relations across Datasets

Jasper Uijlings, Thomas Mensink, Vittorio Ferrari ;

"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."