  In the object detection task, merging various datasets from similar contexts
but with different sets of Objects of Interest (OoI) is an inexpensive way (in
terms of labor cost) for crafting a large-scale dataset covering a wide range
of objects. Moreover, merging datasets allows us to train one integrated object
detector, instead of training several ones, which in turn resulting in the
reduction of computational and time costs. However, merging the datasets from
similar contexts causes samples with partial labeling as each constituent
dataset is originally annotated for its own set of OoI and ignores to annotate
those objects that are become interested after merging the datasets. With the
goal of training \emph{one integrated robust object detector with high
generalization performance}, we propose a training framework to overcome
missing-label challenge of the merged datasets. More specifically, we propose a
computationally efficient self-supervised framework to create on-the-fly
pseudo-labels for the unlabeled positive instances in the merged dataset in
order to train the object detector jointly on both ground truth and pseudo
labels. We evaluate our proposed framework for training Yolo on a simulated
merged dataset with missing rate $\approx\!48\%$ using VOC2012 and VOC2007. We
empirically show that generalization performance of Yolo trained on both ground
truth and the pseudo-labels created by our method is on average $4\%$ higher
than the ones trained only with the ground truth labels of the merged dataset.
