# Temporal Model Adaptation for Person Re-Identification

arXiv: https://arxiv.org/abs/1607.07216 · HTML: https://mlsanity.com/paper/1607.07216/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2016.

## Built on by (most cited first, 5 of 5)

- [Occluded Person Re-Identification with Deep Learning: A Survey and Perspectives](https://mlsanity.com/paper/2311.00603/) (2023)
- [Text-based Person Search in Full Images via Semantic-Driven Proposal Generation](https://mlsanity.com/paper/2109.12965/) (2021)
- [Towards Fewer Labels: Support Pair Active Learning for Person Re-identification](https://mlsanity.com/paper/2204.10008/) (2022)
- [Unsupervised Clustering Active Learning for Person Re-identification](https://mlsanity.com/paper/2112.13308/) (2021)
- [CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face Recognition](https://mlsanity.com/paper/2411.13886/) (2024)

## Abstract (arXiv)

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%.