  We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste
compositional dataset augmentation approach for weakly-supervised instance
segmentation using only image-level supervision. The proposed method consists
of three main components. The first component generates high-quality foreground
object masks. To this end, an EM-like approach is proposed that iteratively
refines an initial set of object mask proposals generated by a generic region
proposal method. Next, in the second component, high-quality context-aware
background images are generated using a text-to-image compositional synthesis
method like DALL-E. Finally, the third component creates a large-scale
pseudo-labeled instance segmentation training dataset by compositing the
foreground object masks onto the original and generated background images. The
proposed approach achieves state-of-the-art weakly-supervised instance
segmentation results on both the PASCAL VOC 2012 and MS COCO datasets by using
only image-level, weak label information. In particular, it outperforms the
best baseline by +7.4 and +2.8 mAP0.50 on PASCAL and COCO, respectively.
Further, the method provides a new solution to the long-tail weakly-supervised
instance segmentation problem (when many classes may only have few training
samples), by selectively augmenting under-represented classes.
