Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations
This work addresses the challenge of accurately linking parts to parent objects in segmentation tasks, which is incremental but improves performance for computer vision applications.
The paper tackles the problem of part-aware panoptic segmentation by proposing TAPPS, a method that jointly predicts object-level and part-level segments using shared queries, which significantly outperforms existing separate prediction methods and achieves new state-of-the-art results.
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.