  The status quo approach to training object detectors requires expensive
bounding box annotations. Our framework takes a markedly different direction:
we transfer tracked object boxes from weakly-labeled videos to weakly-labeled
images to automatically generate pseudo ground-truth boxes, which replace
manually annotated bounding boxes. We first mine discriminative regions in the
weakly-labeled image collection that frequently/rarely appear in the
positive/negative images. We then match those regions to videos and retrieve
the corresponding tracked object boxes. Finally, we design a hough transform
algorithm to vote for the best box to serve as the pseudo GT for each image,
and use them to train an object detector. Together, these lead to
state-of-the-art weakly-supervised detection results on the PASCAL 2007 and
2010 datasets.
