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Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification
The official repository for Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification. We achieve state-of-the-art performances on unsupervised visible-infrared person re-identification task.
Our unified framework
Highlight
- We propose a novel unsupervised learning framework that adopts a bottom-up domain learning strategy with cross-memory association embedding. This enables the model to learn unified representation which is robust against hierarchical discrepancy.
- We design a cross-modality label unification module to propagate and smooth labels between two modalities with heterogeneous affinity matrix and homogeneous structure matrix, respectively, unifying the identities across the two modalities.
- Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that our GUR framework significantly outperforms existing USL-VI-ReID methods, and even surpasses some supervised counterparts, further narrowing the gap between supervised and unsupervised VI-ReID.
Prepare Datasets
Put SYSU-MM01 and RegDB dataset into data/sysu and data/regdb, run prepare_sysu.py and prepare_regdb.py to prepare the training data (convert to market1501 format).
Training
We utilize 4 V100 GPUs for training.
examples:
SYSU-MM01:
- Train:
sh sba_train_sysu.sh
- Test:
sh sba_test_sysu.sh
RegDB:
- Train: :
sh sba_train_regdb.sh
- Test:
sh sba_test_regdb.sh
Citation
This code is based on previous work ADCA. If you find this code useful for your research, please cite our papers.
@InProceedings{Yang_2023_ICCV,
author = {Yang, Bin and Chen, Jun and Ye, Mang},
title = {Towards Grand Unified Representation Learning for Unsupervised Visible-Infrared Person Re-Identification},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {11069-11079}
}
@inproceedings{adca,
title={Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification},
author={Yang, Bin and Ye, Mang and Chen, Jun and Wu, Zesen},
pages = {2843–2851},
booktitle = {ACM MM},
year={2022}
}