  Part-aware panoptic segmentation (PPS) requires (a) that each foreground
object and background region in an image is segmented and classified, and (b)
that all parts within foreground objects are segmented, classified and linked
to their parent object. Existing methods approach PPS by separately conducting
object-level and part-level segmentation. However, their part-level predictions
are not linked to individual parent objects. Therefore, their learning
objective is not aligned with the PPS task objective, which harms the PPS
performance. To solve this, and make more accurate PPS predictions, we propose
Task-Aligned Part-aware Panoptic Segmentation (TAPPS). This method uses a set
of shared queries to jointly predict (a) object-level segments, and (b) the
part-level segments within those same objects. As a result, TAPPS learns to
predict part-level segments that are linked to individual parent objects,
aligning the learning objective with the task objective, and allowing TAPPS to
leverage joint object-part representations. With experiments, we show that
TAPPS considerably outperforms methods that predict objects and parts
separately, and achieves new state-of-the-art PPS results.
