  Co-localization is the problem of localizing objects of the same class using
only the set of images that contain them. This is a challenging task because
the object detector must be built without negative examples that can lead to
more informative supervision signals. The main idea of our method is to cluster
the feature space of a generically pre-trained CNN, to find a set of CNN
features that are consistently and highly activated for an object category,
which we call category-consistent CNN features. Then, we propagate their
combined activation map using superpixel geodesic distances for
co-localization. In our first set of experiments, we show that the proposed
method achieves state-of-the-art performance on three related benchmarks:
PASCAL 2007, PASCAL-2012, and the Object Discovery dataset. We also show that
our method is able to detect and localize truly unseen categories, on six
held-out ImageNet categories with accuracy that is significantly higher than
previous state-of-the-art. Our intuitive approach achieves this success without
any region proposals or object detectors and can be based on a CNN that was
pre-trained purely on image classification tasks without further fine-tuning.
