/LGRL

LOCAL-GLOBAL REPRESENTATION LEARNING FOR ON-THE-FLY FG-SBIR

MIT LicenseMIT

LGRL

LOCAL-GLOBAL REPRESENTATION LEARNING FOR ON-THE-FLY FG-SBIR

demo.mp4

This repository is the official pytorch implementation of our paper, *LOCAL-GLOBAL REPRESENTATION LEARNING FOR ON-THE-FLY FG-SBIR*.

🌟 Pipeline

image-20221026125032094

💾 Dataset

We use two standard datasets as follows:

The QMUL-Shoe-V2 dataset contains 6730 sketches and 2000 photos. A total of 6051 sketches and 1800 photos were used to train the models, and the rest were used to test the models.

The QMUL-Chair-V2 dataset contains 2000 sketches and 400 photos. A total of 1275 sketches and 300 photos were used to train the models, and the rest were used to test the models.

image-20221026125703395

🧪 Example

example

⏳ To Do

  • Release training code

  • Release testing code

  • Release pre-trained models

📔 Citation

coming soon......

💡 Acknowledgments

We would like to thank all of reviewers for their constructive comments and CQUPT for supporting.

📨 Contact

This repo is currently maintained by Dawei Dai (dw_dai@163.com) and his master's student Yingge Liu (s200231105@stu.cqupt.edu.cn).