Bi-Level Implicit Semantic Data Augmentation for Vehicle Re-Identification

Abstract

Vehicle re-identification (Re-ID) aims at finding the target vehicle identity from multi-camera surveillance videos, which plays an important role in the intelligent transportation system (ITS). It suffers from the subtle discrepancy among vehicles from the same vehicle model and large variation across different viewpoints of the same vehicle. To enhance the robustness of Re-ID models, many methods exploit additional detection or segmentation models to extract discriminative local features. Some others employ data-driven methods to enrich the diversity of the training data, such as the data augmentation and 3D-based data generation, so that the Re-ID model can obtain stronger robustness against intra-class variations. However, these methods either rely on extra annotations or greatly increase the computational cost. In this paper, we propose the Bi-level Implicit semantic Data Augmentation (BIDA) framework to solve this problem from two aspects. (1) We implicitly augment the images semantically in the feature space according to the identity-level and superclass-level intra-class variations, which can generate more diverse semantic augmentations beyond the intra-identity variations. (2) We introduce the similarity ranking constraints on the augmented training set by extending the sample-wise triplet loss to the distribution-wise one, which can effectively reduce meaningless semantic transformations and improve the discrimination of the feature. We conduct extensive experiments on VeRi-776, VehicleID and Cityflow benchmarks to reveal the effectiveness of our method. And we achieve new state-of-the-art performance on VeRi-776.