UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces

Official implementation of UniRef++, an extended version of ICCV2023 UniRef.

Highlights

• UniRef/UniRef++ is a unified model for four object segmentation tasks, namely referring image segmentation (RIS), few-shot segmentation (FSS), referring video object segmentation (RVOS) and video object segmentation (VOS).
• At the core of UniRef++ is the UniFusion module for injecting various reference information into network. And we implement it using flash attention with high efficiency.
• UniFusion could play as the plug-in component for foundation models like SAM.

Schedule

• [x] Add Training Guide
• [x] Add Evaluation Guide
• [x] Add Data Preparation
• [x] Release Model Checkpoints
• [x] Release Code

Results

https://github.com/FoundationVision/UniRef/assets/21001460/63d875ed-9f5b-47c9-998f-e83faffedbba

Referring Image Segmentation

Referring Video Object Segmentation

Video Object Segmentation

Zero-shot Video Segmentation & Few-shot Image Segmentation

Model Zoo

Objects365 Pretraining

Imge-joint Training

Video-joint Training

The results are reported on the validation set.

Installation

See INSTALL.md

Getting Started

Please see DATA.md for data preparation.

Please see EVAL.md for evaluation.

Please see TRAIN.md for training.

Citation

If you find this project useful in your research, please consider cite:

@article{wu2023uniref++,
  title={UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces},
  author={Wu, Jiannan and Jiang, Yi and Yan, Bin and Lu, Huchuan and Yuan, Zehuan and Luo, Ping},
  journal={arXiv preprint arXiv:2312.15715},
  year={2023}
}

@inproceedings{wu2023uniref,
  title={Segment Every Reference Object in Spatial and Temporal Spaces},
  author={Wu, Jiannan and Jiang, Yi and Yan, Bin and Lu, Huchuan and Yuan, Zehuan and Luo, Ping},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={2538--2550},
  year={2023}
}

Acknowledgement

The project is based on UNINEXT codebase. We also refer to the repositories Detectron2, Deformable DETR, STCN, SAM. Thanks for their awsome works!