f65f4ba18140de2ad2a2bb1aebcbd1bbfb1f13a8
🌊 SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
This repository is the official implementation of SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory
https://github.com/user-attachments/assets/9d368ca7-2e9b-4fed-9da0-d2efbf620d88
Code
Coming soon!
Acknowledgment
SAMURAI is built on top of SAM 2 by Meta FAIR.
The VOT evaluation code is modifed from VOT Toolkit by Luka Čehovin Zajc.
Citation
Please consider citing our paper and the wonderful SAM 2
if you found our work interesting and useful.
@article{ravi2024sam2,
title={SAM 2: Segment Anything in Images and Videos},
author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
journal={arXiv preprint arXiv:2408.00714},
url={https://arxiv.org/abs/2408.00714},
year={2024}
}
@misc{yang2024samurai,
title={SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory},
author={Cheng-Yen Yang and Hsiang-Wei Huang and Wenhao Chai and Zhongyu Jiang and Jenq-Neng Hwang},
year={2024},
eprint={2411.11922},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2411.11922},
}
Description
This repository is forked from https://github.com/IDEA-Research/Grounded-SAM-2
Languages
Jupyter Notebook
96.6%
Python
3.2%
Cuda
0.2%