S3CE-Net: Spike-guided Spatiotemporal Semantic Coupling and Expansion Network for Long Sequence Event Re-Identification
Journal:
arXiv
Published Date:
May 30, 2025
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:https://github.com/Mhsunshine/SC3E_Net.
