|
--- |
|
license: mit |
|
--- |
|
|
|
# Task-Aligned Part-aware Panoptic Segmentation (TAPPS) |
|
|
|
[[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/)] |
|
|
|
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). |
|
|
|
For the code, see [https://github.com/tue-mps/tapps/](https://github.com/tue-mps/tapps/). |
|
|
|
Please consider citing our work if it is useful for your research. |
|
|
|
``` |
|
@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} |
|
} |
|
``` |
