# Relation Network for Person Re-identification

arXiv: https://arxiv.org/abs/1911.09318 · HTML: https://mlsanity.com/paper/1911.09318/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 29 of 29)

- [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)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification](https://mlsanity.com/paper/2108.00139/) (2021)
- [When Large Vision-Language Models Meet Person Re-Identification](https://mlsanity.com/paper/2411.18111/) (2024)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2308.08887/) (2023)
- [Unleashing the Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification](https://mlsanity.com/paper/2112.00317/) (2021)
- [OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification](https://mlsanity.com/paper/2109.11159/) (2021)
- [Learning to Disentangle Scenes for Person Re-identification](https://mlsanity.com/paper/2111.05476/) (2021)
- [Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification](https://mlsanity.com/paper/2105.12355/) (2021)
- [Benchmarks for Corruption Invariant Person Re-identification](https://mlsanity.com/paper/2111.00880/) (2021)
- [DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-ID](https://mlsanity.com/paper/2411.07205/) (2024)
- [Rethinking Person Re-Identification via Semantic-Based Pretraining](https://mlsanity.com/paper/2110.05074/) (2021)
- [Rethinking Person Re-identification from a Projection-on-Prototypes Perspective](https://mlsanity.com/paper/2308.10717/) (2023)
- [PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders](https://mlsanity.com/paper/2311.04496/) (2023)
- [Part-Attention Based Model Make Occluded Person Re-Identification Stronger](https://mlsanity.com/paper/2404.03443/) (2024)
- [Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2210.03339/) (2022)
- [Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning](https://mlsanity.com/paper/2212.04712/) (2022)
- [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)
- [Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification](https://mlsanity.com/paper/2103.11319/) (2021)
- [Motion-Aware Transformer For Occluded Person Re-identification](https://mlsanity.com/paper/2202.04243/) (2022)
- [Combining human parsing with analytical feature extraction and ranking schemes for high-generalization person reidentification](https://mlsanity.com/paper/2207.14243/) (2022)
- [PGGANet: Pose Guided Graph Attention Network for Person Re-identification](https://mlsanity.com/paper/2111.14411/) (2021)
- [Generalizable Re-Identification from Videos with Cycle Association](https://mlsanity.com/paper/2211.03663/) (2022)
- [End-to-End Context-Aided Unicity Matching for Person Re-identification](https://mlsanity.com/paper/2210.12008/) (2022)
- [Object Re-identification via Spatial-temporal Fusion Networks and Causal Identity Matching](https://mlsanity.com/paper/2408.05558/) (2024)
- [Multi-path Exploration and Feedback Adjustment for Text-to-Image Person Retrieval](https://mlsanity.com/paper/2410.21318/) (2024)

## Abstract (arXiv)

Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown that exploiting local features describing body parts, together with a global feature of a person image itself, gives robust feature representations, even in the case of missing body parts. However, using the individual part-level features directly, without considering relations between body parts, confuses differentiating identities of different persons having similar attributes in corresponding parts. To address this issue, we propose a new relation network for person reID that considers relations between individual body parts and the rest of them. Our model makes a single part-level feature incorporate partial information of other body parts as well, supporting it to be more discriminative. We also introduce a global contrastive pooling (GCP) method to obtain a global feature of a person image. We propose to use contrastive features for GCP to complement conventional max and averaging pooling techniques. We show that our model outperforms the state of the art on the Market1501, DukeMTMC-reID and CUHK03 datasets, demonstrating the effectiveness of our approach on discriminative person representations.