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We present a semi-supervised approach that localizes multiple unknown objectinstances in long videos. We start with a handful of labeled boxes anditeratively learn and label hundreds of thousands of object instances. Wepropose criteria for reliable object detection and tracking for constrainingthe semi-supervised learning process and minimizing semantic drift. Ourapproach does not assume exhaustive labeling of each object instance in anysingle frame, or any explicit annotation of negative data. Working in such ageneric setting allow us to tackle multiple object instances in video, many ofwhich are static. In contrast, existing approaches either do not considermultiple object instances per video, or rely heavily on the motion of theobjects present. The experiments demonstrate the effectiveness of our approachby evaluating the automatically labeled data on a variety of metrics likequality, coverage (recall), diversity, and relevance to training an objectdetector.

Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos