  We introduce DiscoBox, a novel framework that jointly learns instance
segmentation and semantic correspondence using bounding box supervision.
Specifically, we propose a self-ensembling framework where instance
segmentation and semantic correspondence are jointly guided by a structured
teacher in addition to the bounding box supervision. The teacher is a
structured energy model incorporating a pairwise potential and a cross-image
potential to model the pairwise pixel relationships both within and across the
boxes. Minimizing the teacher energy simultaneously yields refined object masks
and dense correspondences between intra-class objects, which are taken as
pseudo-labels to supervise the task network and provide positive/negative
correspondence pairs for dense constrastive learning. We show a symbiotic
relationship where the two tasks mutually benefit from each other. Our best
model achieves 37.9% AP on COCO instance segmentation, surpassing prior weakly
supervised methods and is competitive to supervised methods. We also obtain
state of the art weakly supervised results on PASCAL VOC12 and PF-PASCAL with
real-time inference.
