# Using Auxiliary Information for Person Re-Identification -- A Tutorial Overview

arXiv: https://arxiv.org/abs/2211.08565 · HTML: https://mlsanity.com/paper/2211.08565/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 61 references that are in the map.
- First posted on arXiv 15 Nov 2022.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [Omni-Scale Feature Learning for Person Re-Identification](https://mlsanity.com/paper/1905.00953/) (2019)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer](https://mlsanity.com/paper/2106.04095/) (2021)
- [Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2007.09314/) (2020)
- [Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification](https://mlsanity.com/paper/2002.04114/) (2020)
- [Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/1912.01230/) (2019)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Learning by Aligning: Visible-Infrared Person Re-identification using Cross-Modal Correspondences](https://mlsanity.com/paper/2108.07422/) (2021)
- [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018)
- [AANet: Attribute Attention Network for Person Re-Identifications](https://mlsanity.com/paper/1912.09021/) (2019)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification](https://mlsanity.com/paper/1811.04129/) (2018)
- [Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection](https://mlsanity.com/paper/1702.05552/) (2017)
- [Re-Identification with Consistent Attentive Siamese Networks](https://mlsanity.com/paper/1811.07487/) (2018)
- [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)
- [Real-world Person Re-Identification via Degradation Invariance Learning](https://mlsanity.com/paper/2004.04933/) (2020)
- [Forecasting Future Action Sequences with Neural Memory Networks](https://mlsanity.com/paper/1909.09278/) (2019)
- [Person Re-Identification by Discriminative Selection in Video Ranking](https://mlsanity.com/paper/1601.06260/) (2016)
- [Video-based Person Re-identification via 3D Convolutional Networks and Non-local Attention](https://mlsanity.com/paper/1807.05073/) (2018)
- [Efficient and Deep Person Re-Identification using Multi-Level Similarity](https://mlsanity.com/paper/1803.11353/) (2018)
- [Pedestrian Trajectory Prediction with Structured Memory Hierarchies](https://mlsanity.com/paper/1807.08381/) (2018)

## Abstract (arXiv)

Person re-identification (re-id) is a pivotal task within an intelligent surveillance pipeline and there exist numerous re-id frameworks that achieve satisfactory performance in challenging benchmarks. However, these systems struggle to generate acceptable results when there are significant differences between the camera views, illumination conditions, or occlusions. This result can be attributed to the deficiency that exists within many recently proposed re-id pipelines where they are predominately driven by appearance-based features and little attention is paid to other auxiliary information that could aid the re-id. In this paper, we systematically review the current State-Of-The-Art (SOTA) methods in both uni-modal and multimodal person re-id. Extending beyond a conceptual framework, we illustrate how the existing SOTA methods can be extended to support these additional auxiliary information and quantitatively evaluate the utility of such auxiliary feature information, ranging from logos printed on the objects carried by the subject or printed on the clothes worn by the subject, through to his or her behavioural trajectories. To the best of our knowledge, this is the first work that explores the fusion of multiple information to generate a more discriminant person descriptor and the principal aim of this paper is to provide a thorough theoretical analysis regarding the implementation of such a framework. In addition, using model interpretation techniques, we validate the contributions from different combinations of the auxiliary information versus the original features that the SOTA person re-id models extract. We outline the limitations of the proposed approaches and propose future research directions that could be pursued to advance the area of multi-modal person re-id.