Heterogeneous Relational Complement for Vehicle Re-Identification

Jiajian Zhao, Yifan Zhao, Jia Li, Ke Yan, Yonghong Tian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 205-214


The crucial problem in vehicle re-identification is to find the same vehicle identity when reviewing this object from cross-view cameras, which sets a higher demand for learning viewpoint-invariant representations. In this paper, we propose to solve this problem from two aspects: constructing robust feature representations and proposing camera-sensitive evaluations. We first propose a novel Heterogeneous Relational Complement Network (HRCN) by incorporating region-specific features and cross-level features as complements for the original high-level output. Considering the distributional differences and semantic misalignment, we propose graph-based relation modules to embed these heterogeneous features into one unified high-dimensional space. On the other hand, considering the deficiencies of cross-camera evaluations in existing measures (i.e., CMC and AP), we then propose a Cross-camera Generalization Measure (CGM) to improve the evaluations by introducing position-sensitivity and cross-camera generalization penalties. We further construct a new benchmark of existing models with our proposed CGM and experimental results reveal that our proposed HRCN model achieves new state-of-the-art in VeRi-776, VehicleID, and VERI-Wild.

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@InProceedings{Zhao_2021_ICCV, author = {Zhao, Jiajian and Zhao, Yifan and Li, Jia and Yan, Ke and Tian, Yonghong}, title = {Heterogeneous Relational Complement for Vehicle Re-Identification}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {205-214} }