# Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification

arXiv: https://arxiv.org/abs/1803.09882 · HTML: https://mlsanity.com/paper/1803.09882/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 30 of 55)

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification](https://mlsanity.com/paper/2104.14913/) (2021)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Watching You: Global-guided Reciprocal Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.04337/) (2021)
- [BiCnet-TKS: Learning Efficient Spatial-Temporal Representation for Video Person Re-Identification](https://mlsanity.com/paper/2104.14783/) (2021)
- [Video-based Person Re-identification with Spatial and Temporal Memory Networks](https://mlsanity.com/paper/2108.09039/) (2021)
- [Feature Completion for Occluded Person Re-Identification](https://mlsanity.com/paper/2106.12733/) (2021)
- [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)
- [Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos](https://mlsanity.com/paper/2104.08241/) (2021)
- [Dense Interaction Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2103.09013/) (2021)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification](https://mlsanity.com/paper/2304.14122/) (2023)
- [MEVID: Multi-view Extended Videos with Identities for Video Person Re-Identification](https://mlsanity.com/paper/2211.04656/) (2022)
- [Spatiotemporal Transformer for Video-based Person Re-identification](https://mlsanity.com/paper/2103.16469/) (2021)
- [Wide-Baseline Multi-Camera Calibration using Person Re-Identification](https://mlsanity.com/paper/2104.08568/) (2021)
- [Video-based Person Re-identification with Long Short-Term Representation Learning](https://mlsanity.com/paper/2308.03703/) (2023)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [Deep Learning-based Occluded Person Re-identification: A Survey](https://mlsanity.com/paper/2207.14452/) (2022)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [Video Person Re-identification using Attribute-enhanced Features](https://mlsanity.com/paper/2108.06946/) (2021)
- [OpenAnimals: Revisiting Person Re-Identification for Animals Towards Better Generalization](https://mlsanity.com/paper/2410.00204/) (2024)
- [Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification](https://mlsanity.com/paper/2212.09498/) (2022)
- [From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification](https://mlsanity.com/paper/2408.00096/) (2024)
- [Domain-Class Correlation Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2106.15206/) (2021)
- [MovingFashion: a Benchmark for the Video-to-Shop Challenge](https://mlsanity.com/paper/2110.02627/) (2021)
- [Seq-Masks: Bridging the gap between appearance and gait modeling for video-based person re-identification](https://mlsanity.com/paper/2112.05626/) (2021)

## Abstract (arXiv)

Video-based person re-identification matches video clips of people across non-overlapping cameras. Most existing methods tackle this problem by encoding each video frame in its entirety and computing an aggregate representation across all frames. In practice, people are often partially occluded, which can corrupt the extracted features. Instead, we propose a new spatiotemporal attention model that automatically discovers a diverse set of distinctive body parts. This allows useful information to be extracted from all frames without succumbing to occlusions and misalignments. The network learns multiple spatial attention models and employs a diversity regularization term to ensure multiple models do not discover the same body part. Features extracted from local image regions are organized by spatial attention model and are combined using temporal attention. As a result, the network learns latent representations of the face, torso and other body parts using the best available image patches from the entire video sequence. Extensive evaluations on three datasets show that our framework outperforms the state-of-the-art approaches by large margins on multiple metrics.