# Human-In-The-Loop Person Re-Identification

arXiv: https://arxiv.org/abs/1612.01345 · HTML: https://mlsanity.com/paper/1612.01345/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2016.

## Built on by (most cited first, 19 of 19)

- [NFormer: Robust Person Re-identification with Neighbor Transformer](https://mlsanity.com/paper/2204.09331/) (2022)
- [Lifelong Person Re-Identification via Knowledge Refreshing and Consolidation](https://mlsanity.com/paper/2211.16201/) (2022)
- [FlipReID: Closing the Gap between Training and Inference in Person Re-Identification](https://mlsanity.com/paper/2105.05639/) (2021)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Prompting in the Dark: Assessing Human Performance in Prompt Engineering for Data Labeling When Gold Labels Are Absent](https://mlsanity.com/paper/2502.11267/) (2025)
- [Spatial-Temporal Federated Learning for Lifelong Person Re-identification on Distributed Edges](https://mlsanity.com/paper/2207.11759/) (2022)
- [Effective Generative AI: The Human-Algorithm Centaur](https://mlsanity.com/paper/2406.10942/) (2024)
- [SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2504.09549/) (2025)
- [Active Learning for Animal Re-Identification with Ambiguity-Aware Sampling](https://mlsanity.com/paper/2511.06658/) (2025)
- [Unsupervised Clustering Active Learning for Person Re-identification](https://mlsanity.com/paper/2112.13308/) (2021)
- [Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts](https://mlsanity.com/paper/2307.03003/) (2023)
- [Adapting the re-ID challenge for static sensors](https://mlsanity.com/paper/2412.00290/) (2024)
- [Unsupervised Noisy Tracklet Person Re-identification](https://mlsanity.com/paper/2101.06391/) (2021)
- [Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection](https://mlsanity.com/paper/2203.05395/) (2022)
- [Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2210.11177/) (2022)
- [Mutimodal Ranking Optimization for Heterogeneous Face Re-identification](https://mlsanity.com/paper/2212.05510/) (2022)
- [Benchmarking person re-identification datasets and approaches for practical real-world implementations](https://mlsanity.com/paper/2212.09981/) (2022)
- [Mask-up: Investigating Biases in Face Re-identification for Masked Faces](https://mlsanity.com/paper/2402.13771/) (2024)
- [Exploring Human-in-the-Loop Themes in AI Application Development: An Empirical Thematic Analysis](https://mlsanity.com/paper/2603.05510/) (2026)

## Abstract (arXiv)

Current person re-identification (re-id) methods assume that (1) pre-labelled training data are available for every camera pair, (2) the gallery size for re-identification is moderate. Both assumptions scale poorly to real-world applications when camera network size increases and gallery size becomes large. Human verification of automatic model ranked re-id results becomes inevitable. In this work, a novel human-in-the-loop re-id model based on Human Verification Incremental Learning (HVIL) is formulated which does not require any pre-labelled training data to learn a model, therefore readily scalable to new camera pairs. This HVIL model learns cumulatively from human feedback to provide instant improvement to re-id ranking of each probe on-the-fly enabling the model scalable to large gallery sizes. We further formulate a Regularised Metric Ensemble Learning (RMEL) model to combine a series of incrementally learned HVIL models into a single ensemble model to be used when human feedback becomes unavailable.