论文标题
多域学习和身份挖掘的车辆重新识别
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
论文作者
论文摘要
本文介绍了我们在AI City Challenge 2020（AICITY20）中为Track2的解决方案。 Track2是具有现实数据和合成数据的车辆重新识别（REID）任务。我们的解决方案是基于一个强大的基线，并在REID身上提出了一袋技巧（BOT-B）。首先，我们提出了一种多域学习方法，以结合现实世界和合成数据以训练模型。然后，我们提出了身份挖掘方法来自动生成一部分测试数据的伪标签，这比K-均值聚类更好。具有加权功能的曲目级重新排列策略也用于后处理结果。最后，通过多种模型合奏，我们的方法在地图得分中获得了0.7322，在比赛中获得第三名。这些代码可在https://github.com/heshuting555/aicity2020_dmt_vehiclereid上找到。
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.
