  We present a way to rapidly bootstrap object detection on unseen videos using
minimal human annotations. We accomplish this by combining two complementary
sources of knowledge (one generic and the other specific) using bounding box
merging and model distillation. The first (generic) knowledge source is
obtained from ensembling pre-trained object detectors using a novel bounding
box merging and confidence reweighting scheme. We make the observation that
model distillation with data augmentation can train a specialized detector that
outperforms the noisy labels it was trained on, and train a Student Network on
the ensemble detections that obtains higher mAP than the ensemble itself. The
second (specialized) knowledge source comes from training a detector (which we
call the Supervised Labeler) on a labeled subset of the video to generate
detections on the unlabeled portion. We demonstrate on two popular vehicular
datasets that these techniques work to emit bounding boxes for all vehicles in
the frame with higher mean average precision (mAP) than any of the reference
networks used, and that the combination of ensembled and human-labeled data
produces object detections that outperform either alone.
