KO
|
EN
gitlite — search
Search
#javascript
#python
#hacktoberfest
#react
#ai
#typescript
#llm
#go
#golang
#android
#machine-learning
#rust
#deep-learning
#linux
one-shot-IRSTS
★ 9
Open GitHub ↗
No description available.
Download README (.md)
Explore Similar Repositories
Awesome-Collaborative-Perception-Security
:
Collect some papers focusing on collaborative perception security
ruoyi-h5-vue3
:
基于Vue3.4、Vite5、Vant4、Pinia封装的一套基础模版,仿钉钉工作台,写好了登录/个人信息修改/头像修改/修改密码/的接口对接,可以使用字典管理与RuoYi-Vue后台完美对接。
design-pattern-cpp
:
Code for Design Pattern series.
E-Shop
:
E-Shop is a browser-based billing solution designed to simplify invoicing workflows for retail shops. It helps shop owners quickly log in, manage itemized billing, and generate printable invoices without complex setup
ros_battery_monitoring
:
ros2_control battery sensor interfaces
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
one-shot-IRSTS
?
Download (.md)
# One Shot is Enough for Sequential Infrared Small Target Segmentation ## Introduction The code in this repository is the implementation of the methods and algorithms described in the paper "[One Shot is Enough for Sequential Infrared Small Target Segmentation](https://arxiv.org/abs/2408.04823)". For detailed theoretical background and methodology, please refer to the paper. ## Getting Started To use this code, follow these steps: 1. Download the [IRDST](http://xzbai.buaa.edu.cn/datasets.html) dataset and place it in the `dataset` folder. 2. Download the weight of [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) and place it in the `weights` folder. 3. Install required Libraries. 4. Run the `main.py` file. ## Acknowledgements We are grateful for the resources that have supported this work: - SAM-related code from the [segment-anything](https://github.com/facebookresearch/segment-anything/tree/main) project. - MobileSAM weights available at [MobileSAM](https://github.com/ChaoningZhang/MobileSAM). Thanks to the contributors of these projects for their invaluable contributions. ## Contact For more information or inquiries regarding the one-shot-IRSTS model and its applications, please contact [danbingbing20@mails.ucas.ac.cn].