# Attribute-guided Feature Learning Network for Vehicle Re-identification

arXiv: https://arxiv.org/abs/2001.03872 · HTML: https://mlsanity.com/paper/2001.03872/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Structural Information Guided Multimodal Pre-training for Vehicle-centric Perception](https://mlsanity.com/paper/2312.09812/) (2023)
- [Moving Towards Centers: Re-ranking with Attention and Memory for Re-identification](https://mlsanity.com/paper/2105.01447/) (2021)
- [CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification](https://mlsanity.com/paper/2502.16815/) (2025)
- [A unified framework based on graph consensus term for multi-view learning](https://mlsanity.com/paper/2105.11781/) (2021)
- [Vehicle Re-identification Method Based on Vehicle Attribute and Mutual Exclusion Between Cameras](https://mlsanity.com/paper/2104.14882/) (2021)
- [CEKD:Cross Ensemble Knowledge Distillation for Augmented Fine-grained Data](https://mlsanity.com/paper/2203.06551/) (2022)
- [Strong-TransCenter: Improved Multi-Object Tracking based on Transformers with Dense Representations](https://mlsanity.com/paper/2210.13570/) (2022)
- [Multimodal-Aware Weakly Supervised Metric Learning with Self-weighting Triplet Loss](https://mlsanity.com/paper/2102.02670/) (2021)
- [Locality Relationship Constrained Multi-view Clustering Framework](https://mlsanity.com/paper/2107.05073/) (2021)
- [Self-Supervised Modality-Aware Multiple Granularity Pre-Training for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2112.06147/) (2021)
- [Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification](https://mlsanity.com/paper/2204.13323/) (2022)
- [Complete Solution for Vehicle Re-ID in Surround-view Camera System](https://mlsanity.com/paper/2212.04126/) (2022)
- [Domain Adaptive Person Search via GAN-based Scene Synthesis for Cross-scene Videos](https://mlsanity.com/paper/2308.04322/) (2023)
- [TCGF: A unified tensorized consensus graph framework for multi-view representation learning](https://mlsanity.com/paper/2309.09987/) (2023)

## Abstract (arXiv)

Vehicle re-identification (reID) plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, it poses the critical but challenging problem that is caused by various viewpoints of vehicles, diversified illuminations and complicated environments. Till now, most existing vehicle reID approaches focus on learning metrics or ensemble to derive better representation, which are only take identity labels of vehicle into consideration. However, the attributes of vehicle that contain detailed descriptions are beneficial for training reID model. Hence, this paper proposes a novel Attribute-Guided Network (AGNet), which could learn global representation with the abundant attribute features in an end-to-end manner. Specially, an attribute-guided module is proposed in AGNet to generate the attribute mask which could inversely guide to select discriminative features for category classification. Besides that, in our proposed AGNet, an attribute-based label smoothing (ALS) loss is presented to better train the reID model, which can strength the distinct ability of vehicle reID model to regularize AGNet model according to the attributes. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.