This repository is a wrapper for packaging around the AOT series framework:
- DeAOT: Decoupling Features in Hierarchical Propagation for Video Object Segmentation (NeurIPS 2022, Spotlight) [OpenReview][PDF]
- AOT: Associating Objects with Transformers for Video Object Segmentation (NeurIPS 2021, Score 8/8/7/8) [OpenReview][PDF]
An extension of AOT, AOST (under review), is available now. AOST is a more robust and flexible framework, supporting run-time speed-accuracy trade-offs.
Please consider citing the related paper(s) from the original authors in your publications if it helps your research.
@article{yang2021aost,
title={Scalable Video Object Segmentation with Identification Mechanism},
author={Yang, Zongxin and Miao, Jiaxu and Wei, Yunchao and Wang, Wenguan and Wang, Xiaohan and Yang, Yi},
journal={TPAMI},
year={2024}
}
@inproceedings{xu2023video,
title={Video object segmentation in panoptic wild scenes},
author={Xu, Yuanyou and Yang, Zongxin and Yang, Yi},
booktitle={IJCAI},
year={2023}
}
@inproceedings{yang2022deaot,
title={Decoupling Features in Hierarchical Propagation for Video Object Segmentation},
author={Yang, Zongxin and Yang, Yi},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2022}
}
@inproceedings{yang2021aot,
title={Associating Objects with Transformers for Video Object Segmentation},
author={Yang, Zongxin and Wei, Yunchao and Yang, Yi},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2021}
}
This project is released under the BSD-3-Clause license. See LICENSE for additional details.