# Video-based Person Re-identification Using Spatial-Temporal Attention Networks

arXiv: https://arxiv.org/abs/1810.11261 · HTML: https://mlsanity.com/paper/1810.11261/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Spatio-Temporal Attention Mechanism and Knowledge Distillation for Lip Reading](https://mlsanity.com/paper/2108.03543/) (2021)

## Abstract (arXiv)

We consider the problem of video-based person re-identification. The goal is to identify a person from videos captured under different cameras. In this paper, we propose an efficient spatial-temporal attention based model for person re-identification from videos. Our method generates an attention score for each frame based on frame-level features. The attention scores of all frames in a video are used to produce a weighted feature vector for the input video. Unlike most existing deep learning methods that use global representation, our approach focuses on attention scores. Extensive experiments on two benchmark datasets demonstrate that our method achieves the state-of-the-art performance. This is a technical report.