PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking

📖[Project Page]

This is the official repository for PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking (CVPR 2025).

Introduction

✨ PURA is a test-time adaptation framework for RGB-T tracking, designed to robustly adapt to domain transition scenarios during the testing process.

✨ PURA adapts online through parameter update and recovery mechanisms, avoiding drastic parameter changes and heavy computational burden.

Results

We have released the results of our method on the RGBT234, RGBT210 and GTOT datasets:

Method	RGBT234		RGBT210		GTOT		FPS	Raw Result
	MPR(%)	MSR(%)	PR(%)	SR(%)	MPR(%)	MSR(%)
No Adapt.	90.8	67.6	88.6	65.1	95.1	78.2	59.5	Google Drive
PURA	93.3	70.3	90.3	66.8	95.7	78.6	42.0	Google Drive

• Note: The full code and weights of our method will be released soon.

Usage

For applying PURA to your own RGB-T tracker based on pytracking, you can follow the steps below:

1. Copy pura.py to lib\test\tracker folder.
2. Modify lib\test\tracker\xxx_track.py to include the following code:

from lib.test.tracker import pura  # import PURA
from lib.test.tracker import tent  # import Tent
from lib.test.tracker import eata  # import EATA
from lib.test.tracker import adabn  # import AdaBN
...


class XXXTrack(BaseTracker):
    def __init__(self, params, dataset_name):
        super(XXXTrack, self).__init__(params)
        network = build_xxx_track(params.cfg, training=False)
        network.load_state_dict(torch.load(self.params.checkpoint, map_location='cpu')['net'], strict=True)
        self.cfg = params.cfg
        self.network = network.cuda()
        self.network.eval()

        # PURA
        pura.replace_batchnorm(self.network.box_head)  # replace batchnorm in box_head with PURA
        self.network = pura.configure_model(self.network)  # configure model
        
        # Tent
        # model = tent.configure_model(self.network)
        # tta_params, tta_param_names = tent.collect_params(model.box_head)
        # optimizer = torch.optim.AdamW(tta_params, lr=1e-3)
        # self.model = tent.Tent(model, optimizer)
        
        # ETA
        # model = eata.configure_model(self.network)
        # tta_params, tta_param_names = eata.collect_params(model.box_head)
        # optimizer = torch.optim.SGD(tta_params, lr=0.00025, momentum=0.9)
        # self.model = eata.EATA(model, optimizer, e_margin=math.log(1000)*0.40, d_margin=0.05)
        
        # AdaBN
        # adabn.replace_batchnorm(self.network.box_head)
        # self.network = adabn.configure_model(self.network)

        self.preprocessor = Preprocessor()
        self.state = None
        ...

3. If Tent or ETA is enabled, please build the data as a dictionary input model of the track function in lib\test\tracker\xxx_track.py:

    def track(self, image, info: dict = None):
        H, W, _ = image.shape
        self.frame_id += 1
        
        ...

        with torch.enable_grad():  # Don't forget to enable grad
            model_inputs = {
                "template": cur_template,
                "search": [x_dict.tensors[:, :3, :, :], x_dict.tensors[:, 3:, :, :]],
                "ce_template_mask": self.box_mask_z
            }
            
            out_dict = self.model(model_inputs)

Acknowledgments

We use the implementation of the SVD decomposition from the PGrad repo.

Citation

If our work is helpful for your research, please consider citing our paper:

@inproceedings{shao2025pura,
    title={PURA: Parameter Update-Recovery Test-Time Adaption for RGB-T Tracking},
    author={Shao, Zekai and Hu, Yufan and Fan, Bin and Liu, Hongmin},
    booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
    pages={22089--22098},
    year={2025}
}