Abstract
In this paper, we leverage the advantages of event cameras to resist harsh lighting conditions, reduce background interference, achieve high time resolution, and protect facial information to study the long-sequence event-based person re-identification (Re-ID) task. To this end, we propose a simple and efficient long-sequence event Re-ID model, namely the Spike-guided Spatiotemporal Semantic Coupling and Expansion Network (S3CE-Net). To better handle asynchronous event data, we build S3CE-Net based on spiking neural networks (SNNs). The S3CE-Net incorporates the Spike-guided Spatial-temporal Attention Mechanism (SSAM) and the Spatiotemporal Feature Sampling Strategy (STFS). The SSAM is designed to carry out semantic interaction and association in both spatial and temporal dimensions, leveraging the capabilities of SNNs. The STFS involves sampling spatial feature subsequences and temporal feature subsequences from the spatiotemporal dimensions, driving the Re-ID model to perceive broader and more robust effective semantics. Notably, the STFS introduces no additional parameters and is only utilized during the training stage. Therefore, S3CE-Net is a low-parameter and high-efficiency model for long-sequence event-based person Re-ID. Extensive experiments have verified that our S3CE-Net achieves outstanding performance on many mainstream long-sequence event-based person Re-ID datasets. Code is available at:this https URL.
Abstract (translated)
在这篇论文中，我们利用事件相机的优势来抵抗恶劣的光照条件、减少背景干扰、实现高时间分辨率，并保护面部信息，以研究基于长时间序列事件的人重识别（Re-ID）任务。为此，我们提出了一种简单而高效的长时间序列事件 Re-ID 模型，即 Spike-guided Spatiotemporal Semantic Coupling and Expansion Network (S3CE-Net)。为了更好地处理异步事件数据，我们在脉冲神经网络（SNNs）的基础上构建了 S3CE-Net。S3CE-Net 集成了由 SSAM 和 STFS 构成的组件：即 Spike-guided Spatial-temporal Attention Mechanism (SSAM) 以及 Spatiotemporal Feature Sampling Strategy (STFS)。SSAM 被设计用于在空间和时间两个维度上进行语义交互与关联，利用 SNNs 的能力实现这一目标。而 STFS 则涉及从时空维度中采样空间特征子序列和时间特征子序列，促使 Re-ID 模型能够感知到更广泛且更具鲁棒性的有效语义信息。值得注意的是，STFS 不会引入额外的参数，并仅在训练阶段使用。因此，S3CE-Net 是一种低参数、高效率的长时间序列事件基于的人重识别模型。广泛的实验验证了我们的 S3CE-Net 在许多主流的长时间序列事件基础的人重识别数据集上取得了卓越的表现。代码可在此处获得：[this https URL]。
URL
https://arxiv.org/abs/2505.24401
