How about Person ReID using Example Weighting (OSM&CAA, RLL, ICE, IMAE, DM)?

Dear ReID experts:

If possible, I sincerely recommend you trying our example weighting methods. The reasons: (1) ReID datasets may have noisy observations or labels, and sample imbalance. I find that example weighting is a good approach for addressing these challenges. (2) We can discuss and work together to make it if there is a chance. (3) I am not an expert in ReID, which makes it harder for me to make it alone.

1. ReID using RLL: https://github.com/Qidian213/Ranked_Person_ReID
2. ReID using OSM and CAA: Deep Metric Learning by Online Soft Mining and Class-Aware Attention.

• Code of us: https://github.com/XinshaoAmosWang/OSM_CAA_WeightedContrastiveLoss
• Code by third party: https://github.com/ppriyank/-Online-Soft-Mining-and-Class-Aware-Attention-Pytorch

3. ReID on MARS using IMAE: https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
4. ReID on MARS using DM: https://github.com/XinshaoAmosWang/DerivativeManipulation

Related Papers: OSM and CAA: https://arxiv.org/abs/1811.01459 (Robust metric learning & classification) RLL: https://arxiv.org/abs/1903.03238 (metric learning) IMAE: https://arxiv.org/pdf/1903.12141.pdf (robust classification) DM: https://arxiv.org/pdf/1905.11233.pdf (robust classification and general example weighting)

Hi @Qidian213
I put this post here for open discussion and collaboration. If it is not okay for me, please let me know. Otherwise, I appreciate it greatly. Thanks.

Thanks for your share ! Now i guess align the distance range of positive and negative samples of different categories may help get better performance, but i have no time to study it.