We introduce a class of models named PLEIADES (PoLynomial Expansion In Adaptive Distributed Event-based Systems), which contains temporal convolution kernels generated from orthogonal polynomial basis functions. We focus on interfacing these networks with event-based data to perform online spatiotemporal classification and detection with low latency. By virtue of using structured temporal kernels and event-based data, we have the freedom to vary the sample rate of the data along with the discretization step-size of the network without additional finetuning. We experimented with three event-based benchmarks and obtained state-of-the-art results on all three by large margins with significantly smaller memory and compute costs. We achieved: 1) 99.59% accuracy with 192K parameters on the DVS128 hand gesture recognition dataset and 100% with a small additional output filter; 2) 99.58% test accuracy with 277K parameters on the AIS 2024 eye tracking challenge; and 3) 0.556 mAP with 576k parameters on the PROPHESEE 1 Megapixel Automotive Detection Dataset.

翻译：我们提出了一类名为PLEIADES（PoLynomial Expansion In Adaptive Distributed Event-based Systems）的模型，该类模型包含由正交多项式基函数生成的时间卷积核。我们重点将这些网络与基于事件的数据进行对接，以实现低延迟的在线时空分类与检测。通过利用结构化时间卷积核和基于事件的数据，我们能够自由调整数据采样率以及网络的离散化步长，而无需额外的微调。我们在三个基于事件的基准数据集上进行了实验，以显著更小的内存和计算成本，在三个数据集上均大幅超越了现有最优结果。具体成果包括：1）在DVS128手势识别数据集上以192K参数达到99.59%的准确率，添加一个小的额外输出滤波器后达到100%；2）在AIS 2024眼动追踪挑战中以277K参数达到99.58%的测试准确率；3）在PROPHESEE 1兆像素汽车检测数据集上以576K参数达到0.556 mAP。