Today, state-of-the-art deep neural networks that process event-camera data
first convert a temporal window of events into dense, grid-like input
representations. As such, they exhibit poor generalizability when deployed at
higher inference frequencies (i.e., smaller temporal windows) than the ones
they were trained on. We address this challenge by introducing state-space
models (SSMs) with learnable timescale parameters to event-based vision. This
design adapts to varying frequencies without the need to retrain the network at
different frequencies. Additionally, we investigate two strategies to
counteract aliasing effects when deploying the model at higher frequencies. We
comprehensively evaluate our approach against existing methods based on RNN and
Transformer architectures across various benchmarks, including Gen1 and 1 Mpx
event camera datasets. Our results demonstrate that SSM-based models train 33%
faster and also exhibit minimal performance degradation when tested at higher
frequencies than the training input. Traditional RNN and Transformer models
exhibit performance drops of more than 20 mAP, with SSMs having a drop of 3.31
mAP, highlighting the effectiveness of SSMs in event-based vision tasks.

通过引入具有可学习时间尺度参数的状态空间模型（SSM）来处理事件感测器数据，解决了在不同频率下部署模型的挑战，并且与传统 RNN 和 Transformer 模型相比，SSM 模型在事件感测任务中训练速度更快，且在更高频率的测试输入下表现出较小的性能降低。