# Person Re-identification with Deep Similarity-Guided Graph Neural Network

arXiv: https://arxiv.org/abs/1807.09975 · HTML: https://mlsanity.com/paper/1807.09975/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 30 of 49)

- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Neural Feature Search for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2104.02366/) (2021)
- [Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition](https://mlsanity.com/paper/2104.05160/) (2021)
- [PLIP: Language-Image Pre-training for Person Representation Learning](https://mlsanity.com/paper/2305.08386/) (2023)
- [Large-Scale Pre-training for Person Re-identification with Noisy Labels](https://mlsanity.com/paper/2203.16533/) (2022)
- [Learning Optical Flow with Adaptive Graph Reasoning](https://mlsanity.com/paper/2202.03857/) (2022)
- [Unsupervised Multi-Source Domain Adaptation for Person Re-Identification](https://mlsanity.com/paper/2104.12961/) (2021)
- [Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos](https://mlsanity.com/paper/2104.08241/) (2021)
- [A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics](https://mlsanity.com/paper/2207.12599/) (2022)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2208.00967/) (2022)
- [Wide-Baseline Multi-Camera Calibration using Person Re-Identification](https://mlsanity.com/paper/2104.08568/) (2021)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [Exploring Homogeneous and Heterogeneous Consistent Label Associations for Unsupervised Visible-Infrared Person ReID](https://mlsanity.com/paper/2402.00672/) (2024)
- [Learning Multi-Attention Context Graph for Group-Based Re-Identification](https://mlsanity.com/paper/2104.14236/) (2021)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification](https://mlsanity.com/paper/2112.00317/) (2021)
- [LPGNet: Link Private Graph Networks for Node Classification](https://mlsanity.com/paper/2205.03105/) (2022)
- [HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation](https://mlsanity.com/paper/2211.06235/) (2022)
- [Graph Convolution Based Efficient Re-Ranking for Visual Retrieval](https://mlsanity.com/paper/2306.08792/) (2023)
- [Self-supervised Correlation Mining Network for Person Image Generation](https://mlsanity.com/paper/2111.13307/) (2021)
- [G2DA: Geometry-Guided Dual-Alignment Learning for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2106.07853/) (2021)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [ReMix: Training Generalized Person Re-identification on a Mixture of Data](https://mlsanity.com/paper/2410.21938/) (2024)
- [Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing](https://mlsanity.com/paper/2103.01009/) (2021)
- [Learning Instance-level Spatial-Temporal Patterns for Person Re-identification](https://mlsanity.com/paper/2108.00171/) (2021)
- [Learning through structure: towards deep neuromorphic knowledge graph embeddings](https://mlsanity.com/paper/2109.10376/) (2021)
- [Graph Convolution for Re-ranking in Person Re-identification](https://mlsanity.com/paper/2107.02220/) (2021)
- [The Group Loss++: A deeper look into group loss for deep metric learning](https://mlsanity.com/paper/2204.01509/) (2022)
- [Rethinking Person Re-identification from a Projection-on-Prototypes Perspective](https://mlsanity.com/paper/2308.10717/) (2023)

## Abstract (arXiv)

The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probe-gallery pairs (nodes) and utilizes such relationships to update the probe-gallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes' information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.