Event-based object detection aims to directly recognize and localize objects from asynchronous event streams, facilitating reliable perception in scenarios with fast motion and complex lighting conditions. Its performance largely depends on how temporal information is organized during event sampling. In such dynamic scenarios, fixed sampling strategies struggle to adapt to temporal variations in event streams, whereas recent learnable approaches often exhibit limitations in either their coupling with downstream detection objectives or their ability to fully exploit temporal cues. In this paper, we propose MAS-SNN, a memory-augmented Spiking Neural Network (SNN) framework for event-based object detection, which formulates event sampling as a task-driven, learnable process tightly integrated with representation learning. MAS-SNN introduces a Spiking Memory Attention Embedding (SMAE), composed of a Spiking Memory Buffer (SMB) and a Spiking Memory Attention (SMA) mechanism, to preserve and selectively reuse historical spiking states, thereby fully leveraging the intrinsic temporal dynamics of spiking neurons while maintaining the inherent sparsity and asynchronicity of event-driven vision. MAS-SNN achieves superior performance on N-Caltech 101 and Gen1, demonstrating the effectiveness of memory augmented spiking sampling for event-based object detection.

Video surveillance is widely used in various public spaces, and person re-identification (ReId) based on video frames has become a research hotspot in the field of computer vision. However, most video-based methods struggle with issues such as poor lighting or motion blur, which result in blurry textures and hinder the extraction of discriminative features for specific identities. Additionally, frame-based methods may introduce privacy concerns and suffer from redundant storage between frames. In contrast, bio-inspired sensors—such as event cameras—have asynchronous properties and higher temporal resolution. They record events when changes in light intensity occur, offering a better response to lighting variations in specific scenes. Spiking Neural Networks (SNNs), which transmit information through sparse spikes, are naturally suited to process such asynchronous and sparse event-stream inputs. In this work, we explore the possibility of using only SNN for person ReId based on event-stream data generated by event cameras for the first time. While SNNs for person ReId suffer from issues such as blurry matching and poor feature distinction in spike-based feature matching, we propose an adaptive temporal feature representation (ATFR) method based on membrane potential to address these issues. We evaluate our method on two event-based datasets (Event-ReId and Event-PRID-2011). Compared to existing methods, our model achieves competitive performance while improving energy efficiency by at least 6 times.