# A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification

arXiv: https://arxiv.org/abs/1905.03397 · HTML: https://mlsanity.com/paper/1905.03397/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2019.

## Built on by (most cited first, 30 of 34)

- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [GiT: Graph Interactive Transformer for Vehicle Re-identification](https://mlsanity.com/paper/2107.05475/) (2021)
- [Heterogeneous Relational Complement for Vehicle Re-identification](https://mlsanity.com/paper/2109.07894/) (2021)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Multi-spectral Vehicle Re-identification with Cross-directional Consistency Network and a High-quality Benchmark](https://mlsanity.com/paper/2208.00632/) (2022)
- [Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification](https://mlsanity.com/paper/2301.09498/) (2023)
- [A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges](https://mlsanity.com/paper/2401.10643/) (2024)
- [Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems](https://mlsanity.com/paper/2110.07933/) (2021)
- [Learning Canonical 3D Object Representation for Fine-Grained Recognition](https://mlsanity.com/paper/2108.04628/) (2021)
- [A Synthesis-Based Approach for Thermal-to-Visible Face Verification](https://mlsanity.com/paper/2108.09558/) (2021)
- [CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos](https://mlsanity.com/paper/2306.00075/) (2023)
- [Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review](https://mlsanity.com/paper/2102.09744/) (2021)
- [ES-Net: Erasing Salient Parts to Learn More in Re-Identification](https://mlsanity.com/paper/2103.05918/) (2021)
- [HSGNet: Object Re-identification with Hierarchical Similarity Graph Network](https://mlsanity.com/paper/2211.05486/) (2022)
- [An Empirical Study of Vehicle Re-Identification on the AI City Challenge](https://mlsanity.com/paper/2105.09701/) (2021)
- [A Brief Survey on Person Recognition at a Distance](https://mlsanity.com/paper/2212.08969/) (2022)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment](https://mlsanity.com/paper/2209.07220/) (2022)
- [Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric](https://mlsanity.com/paper/2305.15764/) (2023)
- [Instance and Pair-Aware Dynamic Networks for Re-Identification](https://mlsanity.com/paper/2103.05395/) (2021)
- [Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10401/) (2021)
- [Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification](https://mlsanity.com/paper/2201.02836/) (2022)
- [Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking](https://mlsanity.com/paper/2204.07442/) (2022)
- [Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification](https://mlsanity.com/paper/2204.07943/) (2022)
- [Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification](https://mlsanity.com/paper/2210.01600/) (2022)
- [Spatial-temporal Vehicle Re-identification](https://mlsanity.com/paper/2309.01166/) (2023)
- [VehicleGAN: Pair-flexible Pose Guided Image Synthesis for Vehicle Re-identification](https://mlsanity.com/paper/2311.16278/) (2023)
- [Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification](https://mlsanity.com/paper/2103.05376/) (2021)
- [Center Prediction Loss for Re-identification](https://mlsanity.com/paper/2104.14746/) (2021)
- [Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification](https://mlsanity.com/paper/2204.13323/) (2022)

## Abstract (arXiv)

In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER). The global appearance path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points. Through extensive experimentation, we show that the proposed AAVER method is able to accurately re-identify vehicles in unconstrained scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system is also able to accurately predict vehicle key-points and shows an improvement of more than 7% over state of the art. The code for key-point estimation model is available at https://github.com/Pirazh/Vehicle_Key_Point_Orientation_Estimation.