  We propose to help weakly supervised object localization for classes where
location annotations are not available, by transferring things and stuff
knowledge from a source set with available annotations. The source and target
classes might share similar appearance (e.g. bear fur is similar to cat fur) or
appear against similar background (e.g. horse and sheep appear against grass).
To exploit this, we acquire three types of knowledge from the source set: a
segmentation model trained on both thing and stuff classes; similarity
relations between target and source classes; and co-occurrence relations
between thing and stuff classes in the source. The segmentation model is used
to generate thing and stuff segmentation maps on a target image, while the
class similarity and co-occurrence knowledge help refining them. We then
incorporate these maps as new cues into a multiple instance learning framework
(MIL), propagating the transferred knowledge from the pixel level to the object
proposal level. In extensive experiments, we conduct our transfer from the
PASCAL Context dataset (source) to the ILSVRC, COCO and PASCAL VOC 2007
datasets (targets). We evaluate our transfer across widely different thing
classes, including some that are not similar in appearance, but appear against
similar background. The results demonstrate significant improvement over
standard MIL, and we outperform the state-of-the-art in the transfer setting.
