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@ -35,7 +35,7 @@ The new training/testing protocol split for CUHK03 in our paper is in the "evalu
- cuhk03_new_protocol_config_labeled.mat
================================================================
## IDE Baseline
## IDE Baseline + Re-ranking
### Requirements: Caffe
@ -119,7 +119,7 @@ For Market-1501, these results are better than those reported in our paper, sinc
| | Labeled | Labeled| detected | detected|
| -------| ----- | ---- |---- |---- |
|Methods | Rank@1 | mAP| Rank@1 | mAP|
|LOMO + XQDA | 14.8% | 13.6%|12.8% | 11.5%|
|LOMO + XQDA [1] | 14.8% | 13.6%|12.8% | 11.5%|
|LOMO + XQDA + re-ranking | 19.1% | 20.8%|16.6% | 17.8%|
|IDE_CaffeNet + Euclidean | 15.6% | 14.9%| 15.1% | 14.2%|
|IDE_CaffeNet + Euclidean + re-ranking | 19.1% | 21.3%|19.3% | 20.6%|
@ -130,6 +130,11 @@ For Market-1501, these results are better than those reported in our paper, sinc
|IDE_ResNet_50 + XQDA | 32.0% | 29.6%|31.1% | 28.2%|
|IDE_ResNet_50 + XQDA + re-ranking | 38.1% | 40.3%|34.7% | 37.4%|
### References
[1] Person re-identification by local maximal occurrence representation and metric learning. Liao S, Hu Y, Zhu X, et al. In CVPR. 2015
[2] Sparse contextual activation for efficient visual re-ranking. Bai, Song and Bai, Xiang. IEEE Transactions on Image Processing. 2016
### Contact us
If you have any questions about this code, please do not hesitate to contact us.