# AttributeNet: Attribute Enhanced Vehicle Re-Identification

arXiv: https://arxiv.org/abs/2102.03898 · HTML: https://mlsanity.com/paper/2102.03898/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 41 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net](https://mlsanity.com/paper/1807.09441/) (2018)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [DropBlock: A regularization method for convolutional networks](https://mlsanity.com/paper/1810.12890/) (2018)
- [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020)
- [Style Normalization and Restitution for Generalizable Person Re-identification](https://mlsanity.com/paper/2005.11037/) (2020)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Learning Generalisable Omni-Scale Representations for Person Re-Identification](https://mlsanity.com/paper/1910.06827/) (2019)
- [Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-Identification](https://mlsanity.com/paper/1803.09786/) (2018)
- [Parsing-based View-aware Embedding Network for Vehicle Re-Identification](https://mlsanity.com/paper/2004.05021/) (2020)
- [Vehicle Re-identification with Viewpoint-aware Metric Learning](https://mlsanity.com/paper/1910.04104/) (2019)
- [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://mlsanity.com/paper/1910.10093/) (2019)
- [The Devil is in the Details: Self-Supervised Attention for Vehicle Re-Identification](https://mlsanity.com/paper/2004.06271/) (2020)
- [Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification](https://mlsanity.com/paper/2005.14684/) (2020)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification](https://mlsanity.com/paper/2001.05197/) (2020)
- [PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data](https://mlsanity.com/paper/2005.00673/) (2020)
- [RAM: A Region-Aware Deep Model for Vehicle Re-Identification](https://mlsanity.com/paper/1806.09283/) (2018)
- [Robust Re-Identification by Multiple Views Knowledge Distillation](https://mlsanity.com/paper/2007.04174/) (2020)
- [Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identification](https://mlsanity.com/paper/1910.05549/) (2019)
- [StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification](https://mlsanity.com/paper/2004.12032/) (2020)
- [Attributes Guided Feature Learning for Vehicle Re-identification](https://mlsanity.com/paper/1905.08997/) (2019)
- [Part-Guided Attention Learning for Vehicle Instance Retrieval](https://mlsanity.com/paper/1909.06023/) (2019)
- [Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models](https://mlsanity.com/paper/1807.05618/) (2018)

## Built on by (most cited first, 6 of 6)

- [Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems](https://mlsanity.com/paper/2110.07933/) (2021)
- [Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification](https://mlsanity.com/paper/2310.01129/) (2023)
- [Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey](https://mlsanity.com/paper/2412.00348/) (2024)
- [CLIP-SENet: CLIP-based Semantic Enhancement Network for Vehicle Re-identification](https://mlsanity.com/paper/2502.16815/) (2025)
- [Discriminative-Region Attention and Orthogonal-View Generation Model for Vehicle Re-Identification](https://mlsanity.com/paper/2204.13323/) (2022)
- [ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification](https://mlsanity.com/paper/2311.02734/) (2023)

## Abstract (arXiv)

Vehicle Re-Identification (V-ReID) is a critical task that associates the same vehicle across images from different camera viewpoints. Many works explore attribute clues to enhance V-ReID; however, there is usually a lack of effective interaction between the attribute-related modules and final V-ReID objective. In this work, we propose a new method to efficiently explore discriminative information from vehicle attributes (for instance, color and type). We introduce AttributeNet (ANet) that jointly extracts identity-relevant features and attribute features. We enable the interaction by distilling the ReID-helpful attribute feature and adding it into the general ReID feature to increase the discrimination power. Moreover, we propose a constraint, named Amelioration Constraint (AC), which encourages the feature after adding attribute features onto the general ReID feature to be more discriminative than the original general ReID feature. We validate the effectiveness of our framework on three challenging datasets. Experimental results show that our method achieves the state-of-the-art performance.