Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention
Paper • 2607.18112 • Published
PEMOLA (ICME 2026)
Official checkpoints for Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention (ICME 2026).
Cityscapes-OLAC image-level occlusion annotations are available in the dataset repository. Original Cityscapes images must be obtained separately from the official website.
Download
Download all models from the PEMOLA source repository root:
python -m pip install huggingface_hub
hf download weiwb/PEMOLA --local-dir checkpoints
To download one model, specify its checkpoint filename:
hf download weiwb/PEMOLA mask2former_pemola_coco_olac.pth --local-dir checkpoints
For source code, installation, and inference, see the PEMOLA GitHub repository.
Panoptic Segmentation on COCO-OLAC
| Method | Backbone | PQ | PQTh | PQSt | APThpan | mIoUpan | Weights |
|---|---|---|---|---|---|---|---|
| Mask2Former | ResNet-50 | 40.7 | 44.5 | 35.0 | 30.0 | 54.2 | download |
| + PEMOLA | ResNet-50 | 41.5 | 45.2 | 35.9 | 30.4 | 54.8 | download |
| Mask DINO | ResNet-50 | 44.0 | 48.5 | 37.3 | 33.5 | 53.4 | download |
| + PEMOLA | ResNet-50 | 44.8 | 49.4 | 37.8 | 34.2 | 55.3 | download |
Panoptic Segmentation on Cityscapes-OLAC
| Method | Backbone | PQ | PQTh | PQSt | APThpan | mIoUpan | Weights |
|---|---|---|---|---|---|---|---|
| Mask2Former | ResNet-50 | 61.5 | 54.0 | 66.9 | 35.2 | 76.1 | download |
| + PEMOLA | ResNet-50 | 62.3 | 55.4 | 67.2 | 38.5 | 77.4 | download |
Occlusion Classifier
Top-1 accuracies (%) reported in the paper on the COCO-OLAC test split with background-blackened inputs, for occlusion classification.
| Backbone | Pretraining | Input | Top-1 Acc (%) | Weights |
|---|---|---|---|---|
| Swin-L | ImageNet-22K | 384 | 75.3 | download |
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