  Weakly supervised instance segmentation with image-level labels, instead of
expensive pixel-level masks, remains unexplored. In this paper, we tackle this
challenging problem by exploiting class peak responses to enable a
classification network for instance mask extraction. With image labels
supervision only, CNN classifiers in a fully convolutional manner can produce
class response maps, which specify classification confidence at each image
location. We observed that local maximums, i.e., peaks, in a class response map
typically correspond to strong visual cues residing inside each instance.
Motivated by this, we first design a process to stimulate peaks to emerge from
a class response map. The emerged peaks are then back-propagated and
effectively mapped to highly informative regions of each object instance, such
as instance boundaries. We refer to the above maps generated from class peak
responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed
instance-level representation, which allows instance masks to be extracted even
with some off-the-shelf methods. To the best of our knowledge, we for the first
time report results for the challenging image-level supervised instance
segmentation task. Extensive experiments show that our method also boosts
weakly supervised pointwise localization as well as semantic segmentation
performance, and reports state-of-the-art results on popular benchmarks,
including PASCAL VOC 2012 and MS COCO.
