Research map / Detection and segmentation / Open-set and few-shot detection
Part-Aware Panoptic Segmentation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use panoptic scene labels as the priority instead of requiring agreement with part predictions (ablation) | PPS merging · Cityscapes Panoptic Parts val | PartPQ: 60.2 -> 60.9 | Helped |
| Use panoptic scene labels as the priority instead of requiring agreement with part predictions (ablation) | PPS merging · PASCAL Panoptic Parts validation | PartPQ: 37.5 -> 38.3 | Helped |
| Group semantically similar vehicle and human parts (ablation) | Cityscapes Panoptic Parts · Cityscapes Panoptic Parts val | PartPQ: 55.8 -> 56.9; 60.9 -> 61.4 | Helped |
Closest papers by meaning
- Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations (CVPR 2024 · similarity 0.92)
- Panoptic Segmentation (CVPR 2019 · similarity 0.91)
- Unifying Training and Inference for Panoptic Segmentation (CVPR 2020 · similarity 0.89)
- SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshes (CVPR 2025 · similarity 0.89)
- Real-Time Panoptic Segmentation From Dense Detections (CVPR 2020 · similarity 0.89)
- Scene-Centric Unsupervised Panoptic Segmentation (CVPR 2025 · similarity 0.88)
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