VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification

14 Apr 2020Zhedong ZhengTao RuanYunchao WeiYi YangTao Mei

One fundamental challenge of vehicle re-identification (re-id) is to learn robust and discriminative visual representation, given the significant intra-class vehicle variations across different camera views. As the existing vehicle datasets are limited in terms of training images and viewpoints, we propose to build a unique large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets, and design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet... (read more)

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