Object Perception & Application (Opera) is a unified toolbox for multiple computer vision tasks: detection, segmentation, pose estimation, etc.
To date, Opera implements the following algorithms:
Please refer to get_started.md for installation.
Please see get_started.md for the basic usage of Opera.
Opera is an open source project built upon OpenMMLab. We appreciate all the contributors who implement this flexible and efficient toolkits.
If you find our works useful in your research, please consider citing:
@inproceedings{li2023distilling,
title={Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection},
author={Li, Liangqi and Miao, Jiaxu and Shi, Dahu and Tan, Wenming and Ren, Ye and Yang, Yi and Pu, Shiliang},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={6501--6510},
year={2023}
}
@inproceedings{shi2022end,
title={End-to-End Multi-Person Pose Estimation With Transformers},
author={Shi, Dahu and Wei, Xing and Li, Liangqi and Ren, Ye and Tan, Wenming},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11069--11078},
year={2022}
}
@inproceedings{yu2022soit,
title={SOIT: Segmenting Objects with Instance-Aware Transformers},
author={Yu, Xiaodong and Shi, Dahu and Wei, Xing and Ren, Ye and Ye, Tingqun and Tan, Wenming},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
pages={3188--3196},
year={2022}
}
@inproceedings{shi2021inspose,
title={Inspose: instance-aware networks for single-stage multi-person pose estimation},
author={Shi, Dahu and Wei, Xing and Yu, Xiaodong and Tan, Wenming and Ren, Ye and Pu, Shiliang},
booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
pages={3079--3087},
year={2021}
}This project is released under the Apache 2.0 license.