:unicorn: This is a Generic Object Tracking Project

🔥 Paper Accepted at ICLR 2026!

GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing

📄 Paper and Project Page

• https://chenshihfang.github.io/GOT-EDIT

Human perception for object tracking in a 2D video stream arises from the implicit use of prior visual geometry 🛰️ and semantic reasoning 👁️. GOT-Edit aligns with this principle by enabling trackers to infer 3D geometry from 2D streaming inputs for visual tracking.

The core of this work is cross-modality online model editing. This mechanism performs online constrained model updates to incorporate geometric information adaptively while preserving semantic discrimination for online adaptation under streaming 2D inputs. This paradigm is generalizable across diverse scenarios and environments 🌐. We hope these advances chart a path toward reliability, safety, and social responsibility in vision systems.

Raw Results

The raw results are available for download here

| Dataset | Model | NPr | Suc | Pr | OP50 | OP75 | |---------|---------------|:-----:|:-----:|:---------:|:-----:|:-----:| | NfS-30 | ToMP-50 | 84.00 | 66.86 | 80.58 | 84.36 | 53.50 | | | PiVOT-L | 86.66 | 68.22 | 84.53 | 86.05 | 55.45 | | | GOT-Edit | 87.47 | 71.12 | 86.64 | 89.30 | 59.83 | | LaSOT | ToMP-50 | 77.98 | 67.57 | 72.24 | 79.79 | 65.06 | | | PiVOT-L | 84.68 | 73.37 | 82.09 | 85.64 | 75.18 | | | GOT-Edit | 85.08 | 75.31 | 83.17 | 86.13 | 77.52 | | AVisT | ToMP-50 | 66.66 | 51.61 | 47.74 | 59.47 | 38.88 | | | PiVOT-L | 81.20 | 62.18 | 65.55 | 73.25 | 55.46 | | | GOT-Edit | 82.50 | 64.45 | 68.26 | 74.35 | 59.68 | | OTB-100 | ToMP-50 | 85.98 | 70.07 | 90.83 | 87.83 | 57.79 | | | PiVOT-L | 88.46 | 71.20 | 94.58 | 89.35 | 55.73 | | | GOT-Edit | 91.47 | 74.96 | 97.42 | 93.02 | 63.22 |

Suc: Success Rate AUC
Pr: Precision AUC
NPr: Normalise Precision AUC

Evaluate the Tracking Performance Based on Datasets

python evaluate_GOT_Edit_results.py  

For the GOT-10K and TrackingNet results, please refer to the public leaderboards on the official evaluation websites for both challenges under the entry named “Edit” or “GOT-Edit.” The NfS results follow the evaluation protocol described here.

Prerequisites

The codebase is built based on PyTracking.

Familiarity with the PyTracking codebase will help in understanding the structure of this project.

Installation

Clone the GIT repository.

git clone https://github.com/chenshihfang/GOT.git

Ensure that CUDA 11.7 is installed.

Install dependencies

sudo apt-get install libturbojpeg

Set Up the Dataset Environment

You can follow the setup instructions from PyTracking.

There are two different local.py files located in:

• ltr/admin
• pytracking/evaluation

Set Up the Checkpoint Environment

Updating the checkpoint path in ltr/models/backbone/resnet.py is required. This file includes function calls for both the semantic and geometry backbones.

Training script

Change directory to GOT/pytracking/:

cd GOT/pytracking/
CUDA_VISIBLE_DEVICES=0,1,2,3 python ltr/run_training_dsA.py tomp GOT-Edit_DA3_378

• 💻 Code and pretrained model: More details will be updated soon.

Consider citing “GOT-Edit” if this project impresses you

@inproceedings{gotedit2026iclr,
title     = {{GOT}-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing},
author    = {Shih-Fang Chen and Jun-Cheng Chen and I-Hong Jhuo and Yen-Yu Lin},
booktitle = {Proc. Int. Conf. Learn. Represent. (ICLR)},
year      = {2026}
}

:fire: GOT-JEPA has been accepted at TCSVT 2026! 👇

• GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture

A learning framework that enables dynamic model adaptation in adverse environments and fine-grained occlusion perception.

:fire: PiVOT has been accepted at TMM 2025! 👇

• Improving Visual Object Tracking through Visual Prompting | Code available!

PiVOT proposes a prompt generation network with the pre-trained foundation model CLIP to automatically generate and refine visual prompts, enabling the transfer of foundation model knowledge for tracking.

Please visit here for PiVOT usage details.

Raw Results

The raw results can be downloaded from here.

Run the installation script to install all the dependencies. You need to provide the conda install path and the name for the created conda environment

bash install_PiVOT.sh /your_anaconda3_path/ got_pivot
conda activate got_pivot

Acknowledgement

This codebase is implemented on PyTracking libraries.

Citing

If you find this repository useful, please consider giving a star :star: and a citation

@inproceedings{gotedit2026iclr,
title     = {{GOT}-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing},
author    = {Shih-Fang Chen and Jun-Cheng Chen and I-Hong Jhuo and Yen-Yu Lin},
booktitle = {Proc. Int. Conf. Learn. Represent. (ICLR)},
year      = {2026}
}

@ARTICLE{TCSVT_GOT_JEPA,
title={{GOT}-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture}, 
author={Chen, Shih-Fang and Chen, Jun-Cheng and Jhuo, I-Hong and Lin, Yen-Yu},
journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
year={2026},
doi={10.1109/TCSVT.2026.3675005}
}

@ARTICLE{TMM_PiVOT,
title={Improving Visual Object Tracking Through Visual Prompting}, 
author={Chen, Shih-Fang and Chen, Jun-Cheng and Jhuo, I-Hong and Lin, Yen-Yu},
journal={IEEE Transactions on Multimedia}, 
year={2025},
volume={27},
pages={2682-2694},
doi={10.1109/TMM.2025.3535323}}

Contact:

mail: csf.cs09@nycu.edu.tw