# Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification

arXiv: https://arxiv.org/abs/1908.05669 · HTML: https://mlsanity.com/paper/1908.05669/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

## Built on by (most cited first, 12 of 12)

- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization](https://mlsanity.com/paper/2203.14316/) (2022)
- [A Novel Mix-normalization Method for Generalizable Multi-source Person Re-identification](https://mlsanity.com/paper/2201.09846/) (2022)
- [Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes](https://mlsanity.com/paper/2107.13904/) (2021)
- [Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification](https://mlsanity.com/paper/2203.15210/) (2022)
- [Label Distribution Learning for Generalizable Multi-source Person Re-identification](https://mlsanity.com/paper/2204.05903/) (2022)
- [Semi-supervised Text-based Person Search](https://mlsanity.com/paper/2404.18106/) (2024)
- [Generalizable Metric Network for Cross-domain Person Re-identification](https://mlsanity.com/paper/2306.11991/) (2023)
- [Going Deeper into Semi-supervised Person Re-identification](https://mlsanity.com/paper/2107.11566/) (2021)
- [Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework](https://mlsanity.com/paper/2111.15077/) (2021)
- [Enhancing Multi-Camera Gymnast Tracking Through Domain Knowledge Integration](https://mlsanity.com/paper/2511.16532/) (2025)
- [CLIP-based Camera-Agnostic Feature Learning for Intra-camera Person Re-Identification](https://mlsanity.com/paper/2409.19563/) (2024)

## Abstract (arXiv)

In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when compared with cross-camera labels. In this case, it is very difficult to explore the relationships between cross-camera persons in the training stage due to the lack of cross-camera label information. To deal with this issue, we propose a novel Progressive Cross-camera Soft-label Learning (PCSL) framework for the semi-supervised person Re-ID task, which can generate cross-camera soft-labels and utilize them to optimize the network. Concretely, we calculate an affinity matrix based on person-level features and adapt them to produce the similarities between cross-camera persons (i.e., cross-camera soft-labels). To exploit these soft-labels to train the network, we investigate the weighted cross-entropy loss and the weighted triplet loss from the classification and discrimination perspectives, respectively. Particularly, the proposed framework alternately generates progressive cross-camera soft-labels and gradually improves feature representations in the whole learning course. Extensive experiments on five large-scale benchmark datasets show that PCSL significantly outperforms the state-of-the-art unsupervised methods that employ labeled source domains or the images generated by the GAN-based models. Furthermore, the proposed method even has a competitive performance with respect to deep supervised Re-ID methods.