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| license: mit |
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| # Task-Aligned Part-aware Panoptic Segmentation (TAPPS) |
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| [[Paper](https://openaccess.thecvf.com/content/CVPR2024/papers/de_Geus_Task-aligned_Part-aware_Panoptic_Segmentation_through_Joint_Object-Part_Representations_CVPR_2024_paper.pdf)] [[Project page](http://tue-mps.github.io/tapps)] [[Code](https://github.com/tue-mps/tapps/)] |
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| We provide the models for the part-aware panoptic segmentation task, as presented in our CVPR 2024 paper: [Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations](https://openaccess.thecvf.com/content/CVPR2024/papers/de_Geus_Task-aligned_Part-aware_Panoptic_Segmentation_through_Joint_Object-Part_Representations_CVPR_2024_paper.pdf). |
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| For the code, see [https://github.com/tue-mps/tapps/](https://github.com/tue-mps/tapps/). |
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| Please consider citing our work if it is useful for your research. |
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| ``` |
| @inproceedings{degeus2024tapps, |
| title={{Task-aligned Part-aware Panoptic Segmentation through Joint Object-Part Representations}}, |
| author={{de Geus}, Daan and Dubbelman, Gijs}, |
| booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, |
| year={2024} |
| } |
| ``` |
