# Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

arXiv: https://arxiv.org/abs/2008.11423 · HTML: https://mlsanity.com/paper/2008.11423/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 26 of 26)

- [TransReID: Transformer-based Object Re-Identification](https://mlsanity.com/paper/2102.04378/) (2021)
- [CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels](https://mlsanity.com/paper/2211.13977/) (2022)
- [Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification](https://mlsanity.com/paper/2205.02151/) (2022)
- [State Space Model for New-Generation Network Alternative to Transformers: A Survey](https://mlsanity.com/paper/2404.09516/) (2024)
- [MSINet: Twins Contrastive Search of Multi-Scale Interaction for Object ReID](https://mlsanity.com/paper/2303.07065/) (2023)
- [Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2305.13659/) (2023)
- [Other Tokens Matter: Exploring Global and Local Features of Vision Transformers for Object Re-Identification](https://mlsanity.com/paper/2404.14985/) (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)
- [Rethinking Person Re-identification from a Projection-on-Prototypes Perspective](https://mlsanity.com/paper/2308.10717/) (2023)
- [Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature Alignment](https://mlsanity.com/paper/2405.13781/) (2024)
- [Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10401/) (2021)
- [Weakly-supervised Part-Attention and Mentored Networks for Vehicle Re-Identification](https://mlsanity.com/paper/2107.08228/) (2021)
- [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)
- [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)
- [Collaborative Enhancement Network for Low-quality Multi-spectral Vehicle Re-identification](https://mlsanity.com/paper/2504.14877/) (2025)
- [Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification](https://mlsanity.com/paper/2103.05376/) (2021)
- [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)
- [Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification](https://mlsanity.com/paper/2310.08026/) (2023)
- [Vessel Re-identification and Activity Detection in Thermal Domain for Maritime Surveillance](https://mlsanity.com/paper/2406.08294/) (2024)
- [Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matching](https://mlsanity.com/paper/2408.05558/) (2024)
- [VC-FeS: Viewpoint-Conditioned Feature Selection for Vehicle Re-identification in Thermal Vision](https://mlsanity.com/paper/2605.04750/) (2026)
- [T2I-VeRW: Part-level Fine-grained Perception for Text-to-Image Vehicle Retrieval](https://mlsanity.com/paper/2605.06012/) (2026)

## Abstract (arXiv)

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches.