  The ability to localize and segment objects from unseen classes would open
the door to new applications, such as autonomous object learning in active
vision. Nonetheless, improving the performance on unseen classes requires
additional training data, while manually annotating the objects of the unseen
classes can be labor-extensive and expensive. In this paper, we explore the use
of unlabeled video sequences to automatically generate training data for
objects of unseen classes. It is in principle possible to apply existing video
segmentation methods to unlabeled videos and automatically obtain object masks,
which can then be used as a training set even for classes with no manual labels
available. However, our experiments show that these methods do not perform well
enough for this purpose. We therefore introduce a Bayesian method that is
specifically designed to automatically create such a training set: Our method
starts from a set of object proposals and relies on (non-realistic)
analysis-by-synthesis to select the correct ones by performing an efficient
optimization over all the frames simultaneously. Through extensive experiments,
we show that our method can generate a high-quality training set which
significantly boosts the performance of segmenting objects of unseen classes.
We thus believe that our method could open the door for open-world instance
segmentation using abundant Internet videos.
