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Object detectors tend to perform poorly in new or open domains, and requireexhaustive yet costly annotations from fully labeled datasets. We aim atbenefiting from several datasets with different categories but withoutadditional labelling, not only to increase the number of categories detected,but also to take advantage from transfer learning and to enhance domainindependence.  Our dataset merging procedure starts with training several initial FasterR-CNN on the different datasets while considering the complementary datasets'images for domain adaptation. Similarly to self-training methods, thepredictions of these initial detectors mitigate the missing annotations on thecomplementary datasets. The final OMNIA Faster R-CNN is trained with allcategories on the union of the datasets enriched by predictions. The jointtraining handles unsafe targets with a new classification loss called SoftSigin a softly supervised way.  Experimental results show that in the case of fashion detection for images inthe wild, merging Modanet with COCO increases the final performance from 45.5%to 57.4% in mAP. Applying our soft distillation to the task of detection withdomain shift between GTA and Cityscapes enables to beat the state-of-the-art by5.3 points. Our methodology could unlock object detection for real-worldapplications without immense datasets.

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