GDSTrack

Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking

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Installation

• Clone the repository locally

• Create a conda environment

conda env create -f env.yaml
conda activate GDSTrack
pip install --upgrade git+https://github.com/got-10k/toolkit.git@master
# (You can use `pipreqs ./root` to analyse the requirements of this project.)

Training

• Prepare the training data: We use LMDB to store the training data. Please check ./data/parse_<DATA_NAME> and generate lmdb datasets.
• Specify the paths in ./lib/register/paths.py.

All of our models are trained on a single machine with two RTX3090 GPUs. For distributed training on a single node with 2 GPUs:

• MAT pre-training

python -m torch.distributed.launch --nproc_per_node=2 train.py --experiment=translate_template --train_set=common_pretrain

• Tracker training

Modify the cfg.model.backbone.weights in ./config/cfg_translation_track.py to be the last checkpoint of the MAT pre-training.

python -m torch.distributed.launch --nproc_per_node=2 train.py --experiment=translate_track --train_set=common

Evaluation

We have released the evaluation results on GTOT, RGB-T234, LasHeR, and VTUAV-ST in results.

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Citation

@misc{GDSTrack_2025_IJCAI,
      title={Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking}, 
      author={Shenglan Li and Rui Yao and Yong Zhou and Hancheng Zhu and Kunyang Sun and Bing Liu and Zhiwen Shao and Jiaqi Zhao},
      year={2025},
      eprint={2505.03507},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.03507}, 
}

Acknowledgments

• Thanks for the great MAT, USOT, GMMT.
• For data augmentation, we use Albumentations.

License

This work is released under the GPL 3.0 license. Please see the LICENSE file for more information.