Object Detection With a Unified Label Space From Multiple Datasets
ECCV 2020 | Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical benefits of such an object detector are obvious and significant—application-relevant categories can be picked and merged from arbitrary existing datasets. However, naïve merging of datasets is not possible in this case due to inconsistent object annotations. To address this challenge, we design a framework that works with such partial annotations, and we exploit a pseudo-labeling approach that we adapt for our specific case.