State-Space Models for Efficient and Adaptive Event-Based Object Detection

This paper introduces state-space models (SSMs) as a novel approach to address two key challenges in event-based vision: (1) model performance degradation when operating at temporal frequencies different from training, and (2) slow training efficiency. The proposed SSM-based models demonstrate superior generalization to higher frequencies and achieve a 33% increase in training speed compared to existing recurrent and transformer-based methods.