# RAM: A Region-Aware Deep Model for Vehicle Re-Identification

arXiv: https://arxiv.org/abs/1806.09283 · HTML: https://mlsanity.com/paper/1806.09283/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2018.

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

- [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)
- [Graph Consistency Based Mean-Teaching for Unsupervised Domain Adaptive Person Re-Identification](https://mlsanity.com/paper/2105.04776/) (2021)
- [Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond](https://mlsanity.com/paper/2202.14034/) (2022)
- [A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges](https://mlsanity.com/paper/2401.10643/) (2024)
- [Learning Canonical 3D Object Representation for Fine-Grained Recognition](https://mlsanity.com/paper/2108.04628/) (2021)
- [AttributeNet: Attribute Enhanced Vehicle Re-Identification](https://mlsanity.com/paper/2102.03898/) (2021)
- [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)
- [Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification](https://mlsanity.com/paper/2209.08686/) (2022)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [Black-Box Diagnosis and Calibration on GAN Intra-Mode Collapse: A Pilot Study](https://mlsanity.com/paper/2107.12202/) (2021)
- [Style Variable and Irrelevant Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2209.05235/) (2022)
- [Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric](https://mlsanity.com/paper/2305.15764/) (2023)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [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)
- [Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras](https://mlsanity.com/paper/2104.14882/) (2021)
- [Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks](https://mlsanity.com/paper/2106.10588/) (2021)
- [Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification](https://mlsanity.com/paper/2107.08228/) (2021)
- [Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification](https://mlsanity.com/paper/2201.02836/) (2022)
- [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)
- [Image-to-Video Re-Identification via Mutual Discriminative Knowledge Transfer](https://mlsanity.com/paper/2201.08887/) (2022)
- [Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification](https://mlsanity.com/paper/2204.13323/) (2022)
- [Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning](https://mlsanity.com/paper/2205.10011/) (2022)
- [Complete Solution for Vehicle Re-ID in Surround-view Camera System](https://mlsanity.com/paper/2212.04126/) (2022)
- [Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)](https://mlsanity.com/paper/2303.11169/) (2023)
- [A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification](https://mlsanity.com/paper/2306.14104/) (2023)

## Abstract (arXiv)

Previous works on vehicle Re-ID mainly focus on extracting global features and learning distance metrics. Because some vehicles commonly share same model and maker, it is hard to distinguish them based on their global appearances. Compared with the global appearance, local regions such as decorations and inspection stickers attached to the windshield, may be more distinctive for vehicle Re-ID. To embed the detailed visual cues in those local regions, we propose a Region-Aware deep Model (RAM). Specifically, in addition to extracting global features, RAM also extracts features from a series of local regions. As each local region conveys more distinctive visual cues, RAM encourages the deep model to learn discriminative features. We also introduce a novel learning algorithm to jointly use vehicle IDs, types/models, and colors to train the RAM. This strategy fuses more cues for training and results in more discriminative global and regional features. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.