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ZiRaGroundingDINO

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Official pytorch implementation of ZiRa, a method for incremental vision language object detection (IVLOD)，which has been accepted by NeurIPS 2024.

// repository documentation

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# Grounding DINO Zira
---
Official pytorch implementation of [ZiRa](https://arxiv.org/abs/2403.01680), a method for incremental vision language object detection (IVLOD)

## Install 
If you have a CUDA environment, please make sure the environment variable `CUDA_HOME` is set. We recommend using Python 3.9.

```bash
pip install -e .
```

## Dataset
Download the coco dataset and all Odinw sub-datasets in a folder named "dataset". Then download the GroundingDINO pre-trained model from this [repo](https://github.com/IDEA-Research/GroundingDINO). The Odinw sub-datasets can be downloaded by runing this script.

```bash
python download.py
```

## Training
Run the scripts.

```bash
sh train_odinw13_zira.sh
```

## Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

```bibtex
@article{DBLP:journals/corr/abs-2403-01680,
  author       = {Jieren Deng and
                  Haojian Zhang and
                  Kun Ding and
                  Jianhua Hu and
                  Xingxuan Zhang and
                  Yunkuan Wang},
  title        = {Zero-shot Generalizable Incremental Learning for Vision-Language Object
                  Detection},
  journal      = {CoRR},
  volume       = {abs/2403.01680},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2403.01680},
  doi          = {10.48550/ARXIV.2403.01680},
  eprinttype    = {arXiv},
  eprint       = {2403.01680},
  timestamp    = {Tue, 02 Apr 2024 16:35:34 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2403-01680.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
```

```bibtex
@article{liu2023grounding,
  title={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},
  author={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and Li, Chunyuan and Yang, Jianwei and Su, Hang and Zhu, Jun and others},
  journal={arXiv preprint arXiv:2303.05499},
  year={2023}
}
```

ZiRaGroundingDINO

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