提出动态阈值机制，让事件视觉模型自动稀疏激活，提升能效。
Context-aware Sparse Spatiotemporal Learning for Event-based Vision
- 基于输入分布动态调整神经元阈值，无需手动调参即可实现稀疏激活
- 在事件目标检测和光流估计任务上性能媲美或超越当前最优方法
- 适用于边缘设备的低功耗事件视觉处理，尤其适合类脑计算系统
事件相机作为机器人感知的新兴范式，具备高时间分辨率、高动态范围和对运动模糊的鲁棒性。然而，现有基于深度学习的事件处理方法未能充分挖掘事件数据的稀疏特性，难以部署于资源受限的边缘设备。尽管类脑计算提供高效能替代方案，脉冲神经网络在复杂事件视觉任务（如目标检测和光流估计）中的性能仍不及主流模型。此外，神经网络中实现高激活稀疏性依然困难，通常需精心设计稀疏性损失项。本文提出上下文感知稀疏时空学习（CSSL）框架，引入上下文感知阈值机制，根据输入分布动态调节神经元激活，自然降低激活密度，无需显式稀疏约束。应用于事件目标检测与光流估计任务，CSSL在保持极高神经元稀疏性的同时，性能达到或优于当前最先进方法。实验表明，CSSL对实现高效事件视觉处理具有关键意义，特别适用于类脑计算系统。
原文摘要 · Abstract (English)
Event-based camera has emerged as a promising paradigm for robot perception, offering advantages with high temporal resolution, high dynamic range, and robustness to motion blur. However, existing deep learning-based event processing methods often fail to fully leverage the sparse nature of event data, complicating their integration into resource-constrained edge applications. While neuromorphic computing provides an energy-efficient alternative, spiking neural networks struggle to match of performance of state-of-the-art models in complex event-based vision tasks, like object detection and optical flow. Moreover, achieving high activation sparsity in neural networks is still difficult and often demands careful manual tuning of sparsity-inducing loss terms. Here, we propose Context-aware Sparse Spatiotemporal Learning (CSSL), a novel framework that introduces context-aware thresholding to dynamically regulate neuron activations based on the input distribution, naturally reducing activation density without explicit sparsity constraints. Applied to event-based object detection and optical flow estimation, CSSL achieves comparable or superior performance to state-of-the-art methods while maintaining extremely high neuronal sparsity. Our experimental results highlight CSSL's crucial role in enabling efficient event-based vision for neuromorphic processing.
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