# Exploiting Robust Unsupervised Video Person Re-identification

arXiv: https://arxiv.org/abs/2111.05170 · HTML: https://mlsanity.com/paper/2111.05170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 65 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Deep Mutual Learning](https://mlsanity.com/paper/1706.00384/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Identity-Guided Human Semantic Parsing for Person Re-Identification](https://mlsanity.com/paper/2007.13467/) (2020)
- [Pose-driven Deep Convolutional Model for Person Re-identification](https://mlsanity.com/paper/1709.08325/) (2017)
- [Unsupervised Person Re-identification via Softened Similarity Learning](https://mlsanity.com/paper/2004.03547/) (2020)
- [FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification](https://mlsanity.com/paper/1810.02936/) (2018)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [Pose-guided Visible Part Matching for Occluded Person ReID](https://mlsanity.com/paper/2004.00230/) (2020)
- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification](https://mlsanity.com/paper/1904.00537/) (2019)
- [Unsupervised Tracklet Person Re-Identification](https://mlsanity.com/paper/1903.00535/) (2019)
- [Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification](https://mlsanity.com/paper/1910.10111/) (2019)
- [Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification](https://mlsanity.com/paper/1903.09776/) (2019)
- [Unsupervised Person Re-identification by Deep Learning Tracklet Association](https://mlsanity.com/paper/1809.02874/) (2018)
- [Deep Representation Learning with Part Loss for Person Re-Identification](https://mlsanity.com/paper/1707.00798/) (2017)
- [Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-based Person Re-identification](https://mlsanity.com/paper/2003.12224/) (2020)
- [Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization](https://mlsanity.com/paper/1801.10068/) (2018)
- [Adaptive Graph Representation Learning for Video Person Re-identification](https://mlsanity.com/paper/1909.02240/) (2019)
- [A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification](https://mlsanity.com/paper/1904.03425/) (2019)
- [Relation Network for Person Re-identification](https://mlsanity.com/paper/1911.09318/) (2019)
- [Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer](https://mlsanity.com/paper/1805.04310/) (2018)
- [Dynamic Label Graph Matching for Unsupervised Video Re-Identification](https://mlsanity.com/paper/1709.09297/) (2017)
- [Person Re-identification by Saliency Learning](https://mlsanity.com/paper/1412.1908/) (2014)

## Built on by (most cited first, 3 of 3)

- [Multi-direction and Multi-scale Pyramid in Transformer for Video-based Pedestrian Retrieval](https://mlsanity.com/paper/2202.06014/) (2022)
- [A High-Accuracy Unsupervised Person Re-identification Method Using Auxiliary Information Mined from Datasets](https://mlsanity.com/paper/2205.03124/) (2022)
- [Unleashing the Potential of Tracklets for Unsupervised Video Person Re-Identification](https://mlsanity.com/paper/2406.14261/) (2024)

## Abstract (arXiv)

Unsupervised video person re-identification (reID) methods usually depend on global-level features. And many supervised reID methods employed local-level features and achieved significant performance improvements. However, applying local-level features to unsupervised methods may introduce an unstable performance. To improve the performance stability for unsupervised video reID, this paper introduces a general scheme fusing part models and unsupervised learning. In this scheme, the global-level feature is divided into equal local-level feature. A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning. A global-aware module is proposed to overcome the disadvantages of local-level features. Features from these two modules are fused to form a robust feature representation for each input image. This feature representation has the advantages of local-level feature without suffering from its disadvantages. Comprehensive experiments are conducted on three benchmarks, including PRID2011, iLIDS-VID, and DukeMTMC-VideoReID, and the results demonstrate that the proposed approach achieves state-of-the-art performance. Extensive ablation studies demonstrate the effectiveness and robustness of proposed scheme, local-aware module and global-aware module. The code and generated features are available at https://github.com/deropty/uPMnet.