  Weakly supervised segmentation methods using bounding box annotations focus
on obtaining a pixel-level mask from each box containing an object. Existing
methods typically depend on a class-agnostic mask generator, which operates on
the low-level information intrinsic to an image. In this work, we utilize
higher-level information from the behavior of a trained object detector, by
seeking the smallest areas of the image from which the object detector produces
almost the same result as it does from the whole image. These areas constitute
a bounding-box attribution map (BBAM), which identifies the target object in
its bounding box and thus serves as pseudo ground-truth for weakly supervised
semantic and instance segmentation. This approach significantly outperforms
recent comparable techniques on both the PASCAL VOC and MS COCO benchmarks in
weakly supervised semantic and instance segmentation. In addition, we provide a
detailed analysis of our method, offering deeper insight into the behavior of
the BBAM.
