Papers › Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations

Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations

14 Jun 2024CVPR 2024 1arXiv:2406.10114archive 2025-07-28

Daan de Geus, Gijs Dubbelman

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.

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MLP tue-mps/tapps/tapps/modeling/transformer_decoder/part_decoder.py official repository ran MIT (permissive) · 88342bac1e676835 · report
PartDecoder tue-mps/tapps/tapps/modeling/transformer_decoder/part_decoder.py official repository unverified MIT (permissive) · d719e97ac4f28c87 · report

Tasks

Panoptic SegmentationPart-aware Panoptic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Part-aware Panoptic Segmentation Cityscapes Panoptic Parts TAPPS (Swin-B, COCO pre-training) PartPQ 64.8 #1 of 4 Archive leaderboard report
Part-aware Panoptic Segmentation Pascal Panoptic Parts TAPPS (Swin-B, COCO pre-training) PartPQ 60.4 #1 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

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