Introduction

PyTorch implementation for Noisy-Correspondence Learning for Text-to-Image Person Re-identification (CVPR 2024). The solution to the noisy correspondence problem in TIReID.

News!

• We release the pre-trained model weights and training logs (0% noise), here.
• Accepted by CVPR 2024.
• We will post in VALSE 2024 (Chongqing, China). The poster can be found here.
• We will post in CVPR 2024 (Seattle, Washington, USA). The poster can be found here.

RDE framework

The overview of our RDE. (a) is the illustration of the cross-modal embedding model used in RDE, which consists of basical global embedding (BGE) and token selection embedding (TSE) modules with different granularity. By integrating them, RDE can capture coarse-grained cross-modal interactions while selecting informative local token features to encode more fine-grained representations for a more accurate similarity. (b) shows the core of RDE to achieve robust similarity learning, which consists of Confident Consensus Division (CCD) and Triplet Alignment Loss (TAL). CCD performs consensus division to obtain confident clean training data, thus avoiding misleading from noisy pairs. Unlike traditional Triplet Ranking Loss (TRL), TAL exploits an upper bound to consider all negative pairs, thus embracing more stable learning.

Requirements and Datasets

• Same as IRRA

Noise index

If you want to experiment with the same noise index as in the paper, the noise index files can be found in 2024-CVPR-RDE/noiseindex.

Training and Evaluation

Training new models

sh run_rde.sh

Evaluation

Modify the sub in the test.py file and run it.

python test.py

Experiment Results:

Citation

If RDE is useful for your research, you can cite the following papers:

@inproceedings{qin2024noisy,
  title={Noisy-Correspondence Learning for Text-to-Image Person Re-identification},
  author={Qin, Yang and Chen, Yingke and Peng, Dezhong and Peng, Xi and Zhou, Joey Tianyi and Hu, Peng},
  booktitle={IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2024},
}

@article{qin2023cross,
  title={Cross-modal Active Complementary Learning with Self-refining Correspondence},
  author={Qin, Yang and Sun, Yuan and Peng, Dezhong and Zhou, Joey Tianyi and Peng, Xi and Hu, Peng},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2023}
}

@inproceedings{qin2022deep,
  title={Deep evidential learning with noisy correspondence for cross-modal retrieval},
  author={Qin, Yang and Peng, Dezhong and Peng, Xi and Wang, Xu and Hu, Peng},
  booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
  pages={4948--4956},
  year={2022}
}

License

Apache License 2.0

Acknowledgements

The code is based on IRRA licensed under Apache 2.0.