Name
Abstract | ||
|---|---|---|
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering. The tracklet-level re-ranking strategy with weighted features is also used to post-process the results. Finally, with multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition. The codes are available at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/CVPRW50498.2020.00299 | CVPR Workshops |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| He Shuting | 1 | 0 | 0.34 |
| Hao Luo | 2 | 123 | 10.02 |
| Chen Weihua | 3 | 0 | 0.34 |
| Zhang Miao | 4 | 0 | 0.34 |
| Yuqi Zhang | 5 | 4 | 7.17 |
| fan wang | 6 | 15 | 16.24 |
| Hao Li | 7 | 261 | 85.92 |
| Jiang Wei | 8 | 14 | 2.79 |