DyML-Vehicle (Dynamic Metric Learning Vehicle)
Introduced by Sun et al. in Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
DyML-Vehicle merges two vehicle re-ID datasets PKU VehicleID [1], VERI-Wild [1]. Since these two datasets have only annotations on the identity (fine) level, we manually annotate each image with “model” label (e.g., Toyota Camry, Honda Accord, Audi A4) and “body type” label (e.g., car, suv, microbus, pickup). Moreover, we label all the taxi images as a novel testing class under coarse level.
[1] Hongye Liu, Yonghong Tian, Yaowei Wang, Lu Pang, and Tiejun Huang. Deep relative distance learning: Tell the difference between similar vehicles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2167–2175, 2016. 4
[2] Y. Lou, Y. Bai, J. Liu, S. Wang, and L. Duan. Veri-wild: A large dataset and a new method for vehicle re-identification in the wild. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3230–3238, 2019. 4
Papers
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