OMNIA Faster R-CNN: Detection in the wild through dataset merging and soft distillation

Understand

Object detectors tend to perform poorly in new or open domains, and require exhaustive yet costly annotations from fully labeled datasets.

• We aim at benefiting from several datasets with different categories but without additional labelling, not only to increase the number of categories detected, but also to take advantage from transfer learning and to enhance domain independence.
• Our dataset merging procedure starts with training several initial Faster R-CNN on the different datasets while considering the complementary datasets' images for domain adaptation.
• Similarly to self-training methods, the predictions of these initial detectors mitigate the missing annotations on the complementary datasets.