# Adaptive Graph Representation Learning for Video Person Re-identification

arXiv: https://arxiv.org/abs/1909.02240 · HTML: https://mlsanity.com/paper/1909.02240/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2019.

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

- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Action Scene Graphs for Long-Form Understanding of Egocentric Videos](https://mlsanity.com/paper/2312.03391/) (2023)
- [A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification](https://mlsanity.com/paper/2104.01745/) (2021)
- [Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos](https://mlsanity.com/paper/2104.08241/) (2021)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [When Video Classification Meets Incremental Classes](https://mlsanity.com/paper/2106.15827/) (2021)
- [MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification](https://mlsanity.com/paper/2211.04656/) (2022)
- [Spatiotemporal Transformer for Video-based Person Re-identification](https://mlsanity.com/paper/2103.16469/) (2021)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification](https://mlsanity.com/paper/2105.11722/) (2021)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [Open-Set Biometrics: Beyond Good Closed-Set Models](https://mlsanity.com/paper/2407.16133/) (2024)
- [Graph Convolution Based Efficient Re-Ranking for Visual Retrieval](https://mlsanity.com/paper/2306.08792/) (2023)
- [A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognition](https://mlsanity.com/paper/2508.00053/) (2025)
- [HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID](https://mlsanity.com/paper/2508.05038/) (2025)
- [MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification](https://mlsanity.com/paper/2110.05886/) (2021)
- [Self-supervised Correlation Mining Network for Person Image Generation](https://mlsanity.com/paper/2111.13307/) (2021)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [Video Person Re-identification using Attribute-enhanced Features](https://mlsanity.com/paper/2108.06946/) (2021)
- [Not Every Patch is Needed: Towards a More Efficient and Effective Backbone for Video-based Person Re-identification](https://mlsanity.com/paper/2501.16811/) (2025)
- [Graph Convolution for Re-ranking in Person Re-identification](https://mlsanity.com/paper/2107.02220/) (2021)
- [Colors See Colors Ignore: Clothes Changing ReID with Color Disentanglement](https://mlsanity.com/paper/2507.07230/) (2025)
- [Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network](https://mlsanity.com/paper/2209.11582/) (2022)
- [Clothes-Changing Person Re-identification Based On Skeleton Dynamics](https://mlsanity.com/paper/2503.10759/) (2025)
- [FusionAgent: A Multimodal Agent with Dynamic Model Selection for Human Recognition](https://mlsanity.com/paper/2603.26908/) (2026)
- [Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identification](https://mlsanity.com/paper/2109.08811/) (2021)
- [Exploiting Robust Unsupervised Video Person Re-identification](https://mlsanity.com/paper/2111.05170/) (2021)
- [Deep Learning for Video-based Person Re-Identification: A Survey](https://mlsanity.com/paper/2303.11332/) (2023)
- [Attention-based Shape and Gait Representations Learning for Video-based Cloth-Changing Person Re-Identification](https://mlsanity.com/paper/2402.03716/) (2024)
- [Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification](https://mlsanity.com/paper/2207.14243/) (2022)

## Abstract (arXiv)

Recent years have witnessed the remarkable progress of applying deep learning models in video person re-identification (Re-ID). A key factor for video person Re-ID is to effectively construct discriminative and robust video feature representations for many complicated situations. Part-based approaches employ spatial and temporal attention to extract representative local features. While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features. Specifically, we exploit the pose alignment connection and the feature affinity connection to construct an adaptive structure-aware adjacency graph, which models the intrinsic relations between graph nodes. We perform feature propagation on the adjacency graph to refine regional features iteratively, and the neighbor nodes' information is taken into account for part feature representation. To learn compact and discriminative representations, we further propose a novel temporal resolution-aware regularization, which enforces the consistency among different temporal resolutions for the same identities. We conduct extensive evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and DukeMTMC-VideoReID, experimental results achieve the competitive performance which demonstrates the effectiveness of our proposed method. The code is available at https://github.com/weleen/AGRL.pytorch.