Efficiently selecting an appropriate spike stream data length to extract
precise information is the key to the spike vision tasks. To address this
issue, we propose a dynamic timing representation for spike streams. Based on
multi-layers architecture, it applies dilated convolutions on temporal
dimension to extract features on multi-temporal scales with few parameters. And
we design layer attention to dynamically fuse these features. Moreover, we
propose an unsupervised learning method for optical flow estimation in a
spike-based manner to break the dependence on labeled data. In addition, to
verify the robustness, we also build a spike-based synthetic validation dataset
for extreme scenarios in autonomous driving, denoted as SSES dataset. It
consists of various corner cases. Experiments show that our method can predict
optical flow from spike streams in different high-speed scenes, including real
scenes. For instance, our method gets $15\%$ and $19\%$ error reduction from
the best spike-based work, SCFlow, in $\Delta t=10$ and $\Delta t=20$
respectively which are the same settings as the previous works.

本文提出了动态时间表示的脉冲流方法，能够有效地选择适当的脉冲流数据长度来提取精确信息，并通过多层卷积来提取多时间尺度特征，从而应用于光流估计等脉冲视觉任务。同时，文中介绍了一种无监督学习方法进行光流估计，并构建了一个脉冲流驱动的合成数据集进行验证。实验表明，该方法可以在不同高速场景下从脉冲流中预测光流，并且相较于先前方法可以获得较高的准确性。