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.

使用边界框监督，以结构化 teacher 模型为指导，共同学习实例分割和语义对应的 DiscoBox 框架，同时又能为密集对比学习提供正 / 负的对应对，用以监督网络任务实现物体掩模细化和类内对象间的密集对应，并取得了和有监督方法相当的 37.0% 的 COCO 实例分割性能和最新的 PASCAL VOC12 以及 PF-PASCAL 的最高弱监督结果的实时推理。