计算机科学
人工神经网络
常量(计算机编程)
时间常数
人工智能
跟踪(教育)
尖峰神经网络
事件(粒子物理)
模式识别(心理学)
计算机视觉
物理
工程类
电气工程
程序设计语言
量子力学
教育学
心理学
作者
Jiqing Zhang,Malu Zhang,Yuanchen Wang,Qianhui Liu,Baocai Yin,Haizhou Li,Xin Yang
标识
DOI:10.1109/tip.2025.3533213
摘要
The brain-inspired Spiking Neural Networks (SNNs) work in an event-driven manner and have an implicit recurrence in neuronal membrane potential to memorize information over time, which are inherently suitable to handle temporal event-based streams. Despite their temporal nature and recent approaches advancements, these methods have predominantly been assessed on event-based classification tasks. In this paper, we explore the utility of SNNs for event-based tracking tasks. Specifically, we propose a brain-inspired adaptive Leaky Integrate-and-Fire neuron (BA-LIF) that can adaptively adjust the membrane time constant according to the inputs, thereby accelerating the leakage of meaningless noise features and reducing the decay of valuable information. SNNs composed of our proposed BA-LIF neurons can achieve high performance without a careful and time-consuming trial-by-error initialization on the membrane time constant. The adaptive capability of our network is further improved by introducing an extra temporal feature aggregator (TFA) that assigns attention weights over the temporal dimension. Extensive experiments on various event-based tracking datasets validate the effectiveness of our proposed method. We further validate the generalization capability of our method by applying it to other event-classification tasks.
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