SpikeTrack: 一种用于高效视觉跟踪的脉冲驱动框架
SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking
AI总结：
SpikeTrack是一种基于脉冲驱动的高效视觉跟踪框架，通过非对称设计和记忆检索模块，在保持精度的同时实现能量高效。
摘要：
脉冲神经网络（SNNs）承诺了能量高效的视觉处理，但将其应用于RGB视觉跟踪仍然困难：现有的SNN跟踪框架要么未能完全与脉冲驱动计算对齐，要么未能充分利用神经元的时空动态，导致效率与精度之间的权衡。为此，我们引入了SpikeTrack，一种用于能量高效RGB物体跟踪的脉冲驱动框架。SpikeTrack采用了一种新颖的非对称设计，利用非对称时间步扩展和单向信息流，利用时空动态的同时减少计算。为了确保分支之间有效的单向信息传输，我们设计了一个受神经推理机制启发的记忆检索模块。该模块通过递归查询由模板初始化的紧凑记忆来检索目标线索，并随时间推移增强目标感知。广泛的实验表明，SpikeTrack在SNN基于的跟踪器中达到了最先进的水平，并且在先进的ANN跟踪器中保持竞争力。值得注意的是，它在LaSOT数据集上超越了TransT，同时仅消耗其1/26的能量。据我们所知，SpikeTrack是首个使RGB跟踪既准确又节能的脉冲驱动框架。代码和模型可在https://github.com/faicaiwawa/SpikeTrack上获得。
英文摘要：
Spiking Neural Networks (SNNs) promise energy-efficient vision, but applying them to RGB visual tracking remains difficult: Existing SNN tracking frameworks either do not fully align with spike-driven computation or do not fully leverage neurons' spatiotemporal dynamics, leading to a trade-off between efficiency and accuracy. To address this, we introduce SpikeTrack, a spike-driven framework for energy-efficient RGB object tracking. SpikeTrack employs a novel asymmetric design that uses asymmetric timestep expansion and unidirectional information flow, harnessing spatiotemporal dynamics while cutting computation. To ensure effective unidirectional information transfer between branches, we design a memory-retrieval module inspired by neural inference mechanisms. This module recurrently queries a compact memory initialized by the template to retrieve target cues and sharpen target perception over time. Extensive experiments demonstrate that SpikeTrack achieves the state-of-the-art among SNN-based trackers and remains competitive with advanced ANN trackers. Notably, it surpasses TransT on LaSOT dataset while consuming only 1/26 of its energy. To our knowledge, SpikeTrack is the first spike-driven framework to make RGB tracking both accurate and energy efficient. The code and models are available at https://github.com/faicaiwawa/SpikeTrack.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
