  Weakly-supervised object localization methods tend to fail for object classes
that consistently co-occur with the same background elements, e.g. trains on
tracks. We propose a method to overcome these failures by adding a very small
amount of model-specific additional annotation. The main idea is to cluster a
deep network's mid-level representations and assign object or distractor labels
to each cluster. Experiments show substantially improved localization results
on the challenging ILSVC2014 dataset for bounding box detection and the PASCAL
VOC2012 dataset for semantic segmentation.
