# Global-Local Temporal Representations For Video Person Re-Identification

arXiv: https://arxiv.org/abs/1908.10049 · HTML: https://mlsanity.com/paper/1908.10049/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2019.

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

- [Spatio-Temporal Representation Factorization for Video-based Person Re-Identification](https://mlsanity.com/paper/2107.11878/) (2021)
- [A Video Is Worth Three Views: Trigeminal Transformers for Video-based Person Re-identification](https://mlsanity.com/paper/2104.01745/) (2021)
- [A Survey on Video Moment Localization](https://mlsanity.com/paper/2306.07515/) (2023)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification](https://mlsanity.com/paper/2103.11319/) (2021)
- [Context Sensing Attention Network for Video-based Person Re-identification](https://mlsanity.com/paper/2207.02631/) (2022)
- [Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume](https://mlsanity.com/paper/2208.03028/) (2022)
- [Learning to Learn Transferable Generative Attack for Person Re-Identification](https://mlsanity.com/paper/2409.04208/) (2024)
- [Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification](https://mlsanity.com/paper/2101.04340/) (2021)
- [Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection](https://mlsanity.com/paper/2312.01764/) (2023)
- [Skeleton-Guided Spatial-Temporal Feature Learning for Video-Based Visible-Infrared Person Re-Identification](https://mlsanity.com/paper/2411.11069/) (2024)
- [KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos](https://mlsanity.com/paper/2507.07393/) (2025)

## Abstract (arXiv)

This paper proposes the Global-Local Temporal Representation (GLTR) to exploit the multi-scale temporal cues in video sequences for video person Re-Identification (ReID). GLTR is constructed by first modeling the short-term temporal cues among adjacent frames, then capturing the long-term relations among inconsecutive frames. Specifically, the short-term temporal cues are modeled by parallel dilated convolutions with different temporal dilation rates to represent the motion and appearance of pedestrian. The long-term relations are captured by a temporal self-attention model to alleviate the occlusions and noises in video sequences. The short and long-term temporal cues are aggregated as the final GLTR by a simple single-stream CNN. GLTR shows substantial superiority to existing features learned with body part cues or metric learning on four widely-used video ReID datasets. For instance, it achieves Rank-1 Accuracy of 87.02% on MARS dataset without re-ranking, better than current state-of-the art.