tags:
- pemola
- panoptic-segmentation
- occlusion
- icme-2026
datasets:
- weiwb/COCO-OLAC
- weiwb/Cityscapes-OLAC
PEMOLA (ICME 2026)
Official checkpoints for Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention (ICME 2026).
Related resources
Research overview: Occlusion-Aware Visual Understanding. Explore the papers, datasets, model checkpoints, and how they relate.
| Resource | What it provides |
|---|---|
| COCO-OLAC dataset | Image-level occlusion annotations for 30,000 COCO training images and 5,000 validation images, including validation subsets by occlusion level. |
| Cityscapes-OLAC dataset | Image-level occlusion annotations for 2,975 Cityscapes training images and 500 validation images, following the same annotation protocol. |
Both dataset repositories provide additional occlusion annotations. Obtain the original images and segmentation annotations separately from the official COCO website and the official Cityscapes 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 |