# Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification

arXiv: https://arxiv.org/abs/2103.05376 · HTML: https://mlsanity.com/paper/2103.05376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [Semi-Supervised Sequence Modeling with Cross-View Training](https://mlsanity.com/paper/1809.08370/) (2018)
- [Batch DropBlock Network for Person Re-identification and Beyond](https://mlsanity.com/paper/1811.07130/) (2018)
- [Vehicle Re-identification with Viewpoint-aware Metric Learning](https://mlsanity.com/paper/1910.04104/) (2019)
- [The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification](https://mlsanity.com/paper/2004.06271/) (2020)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification](https://mlsanity.com/paper/1905.03397/) (2019)
- [RAM: A Region-Aware Deep Model for Vehicle Re-Identification](https://mlsanity.com/paper/1806.09283/) (2018)
- [Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network](https://mlsanity.com/paper/2008.11423/) (2020)
- [Vehicle Re-identification in Aerial Imagery: Dataset and Approach](https://mlsanity.com/paper/1904.01400/) (2019)
- [Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences](https://mlsanity.com/paper/2004.05749/) (2020)
- [Vehicle Re-identification Using Quadruple Directional Deep Learning Features](https://mlsanity.com/paper/1811.05163/) (2018)
- [Vehicle Re-Identification in Context](https://mlsanity.com/paper/1809.09409/) (2018)
- [Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification](https://mlsanity.com/paper/1812.04239/) (2018)
- [Variational Representation Learning for Vehicle Re-Identification](https://mlsanity.com/paper/1905.02343/) (2019)
- [Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond](https://mlsanity.com/paper/2002.09274/) (2020)
- [Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification](https://mlsanity.com/paper/2003.07139/) (2020)

## Abstract (arXiv)

Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under different viewpoints. To this end, most existing approaches resort to the supervised cross-view learning using extensive extra viewpoints annotations, which however, is difficult to deploy in real applications due to the expensive labelling cost and the continous viewpoint variation that makes it hard to define discrete viewpoint labels. In this study, we present a pluggable Weakly-supervised Cross-View Learning (WCVL) module for vehicle ReID. Through hallucinating the cross-view samples as the hardest positive counterparts in feature domain, we can learn the consistent feature representation via minimizing the cross-view feature distance based on vehicle IDs only without using any viewpoint annotation. More importantly, the proposed method can be seamlessly plugged into most existing vehicle ReID baselines for cross-view learning without re-training the baselines. To demonstrate its efficacy, we plug the proposed method into a bunch of off-the-shelf baselines and obtain significant performance improvement on four public benchmark datasets, i.e., VeRi-776, VehicleID, VRIC and VRAI.