# SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification

arXiv: https://arxiv.org/abs/1807.05688 · HTML: https://mlsanity.com/paper/1807.05688/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2018.

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

- [Attention Mechanisms in Computer Vision: A Survey](https://mlsanity.com/paper/2111.07624/) (2021)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN](https://mlsanity.com/paper/2309.01439/) (2023)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Person Re-identification via Attention Pyramid](https://mlsanity.com/paper/2108.05340/) (2021)
- [Video Joint Modelling Based on Hierarchical Transformer for Co-summarization](https://mlsanity.com/paper/2112.13478/) (2021)
- [A Spatial-channel-temporal-fused Attention for Spiking Neural Networks](https://mlsanity.com/paper/2209.10837/) (2022)
- [Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification](https://mlsanity.com/paper/2301.00531/) (2023)
- [Deep video representation learning: a survey](https://mlsanity.com/paper/2405.06574/) (2024)
- [Local-Global Associative Frame Assemble in Video Re-ID](https://mlsanity.com/paper/2110.12018/) (2021)
- [Stronger Baseline for Person Re-Identification](https://mlsanity.com/paper/2112.01059/) (2021)

## Abstract (arXiv)

Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representations, and c) developing more effective distance metrics. To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN). It has several appealing properties. First, SCAN adopts non-parametric attention mechanism to refine the intra-sequence and inter-sequence feature representation of videos, and outputs self-and-collaborative feature representation for each video, making the discriminative frames aligned between the probe and gallery sequences.Second, beyond existing models, a generalized pairwise similarity measurement is proposed to calculate the similarity feature representations of video pairs, enabling computing the matching scores by the binary classifier. Third, a dense clip segmentation strategy is also introduced to generate rich probe-gallery pairs to optimize the model. Extensive experiments demonstrate the effectiveness of SCAN, which outperforms the best-performing baselines on iLIDS-VID, PRID2011 and MARS dataset, respectively.