Spiking Neural Network as Adaptive Event Stream Slicer

TL;DR

SpikeSlicer introduces an adaptive, low-energy spiking neural network-based method for event stream slicing that enhances event-based object tracking and recognition by preserving crucial temporal information.

Contribution

It presents a novel SNN-based event slicer with SPA-Loss and feedback training, enabling adaptive event stream segmentation for improved downstream tasks.

Findings

Significant performance improvements in event-based object tracking.

Effective SNN-ANN cooperation paradigm demonstrated.

Low-energy, adaptive event slicing outperforms fixed grouping methods.

Abstract

Event-based cameras are attracting significant interest as they provide rich edge information, high dynamic range, and high temporal resolution. Many state-of-the-art event-based algorithms rely on splitting the events into fixed groups, resulting in the omission of crucial temporal information, particularly when dealing with diverse motion scenarios (\eg, high/low speed).In this work, we propose SpikeSlicer, a novel-designed plug-and-play event processing method capable of splitting events stream adaptively.SpikeSlicer utilizes a low-energy spiking neural network (SNN) to trigger event slicing. To guide the SNN to fire spikes at optimal time steps, we propose the Spiking Position-aware Loss (SPA-Loss) to modulate the neuron's state. Additionally, we develop a Feedback-Update training strategy that refines the slicing decisions using feedback from the downstream artificial neural network (ANN). Extensive experiments demonstrate that our method yields significant performance improvements in event-based object tracking and recognition. Notably, SpikeSlicer provides a brand-new SNN-ANN cooperation paradigm, where the SNN acts as an efficient, low-energy data processor to assist the ANN in improving downstream performance, injecting new perspectives and potential avenues of exploration. Our code is available at https://github.com/AndyCao1125/SpikeSlicer.