论文标题
将密集的对象检测模型传输到基于事件的数据
Transferring dense object detection models to event-based data
论文作者
论文摘要
基于事件的图像表示与传统密集图像根本不同。这构成了将当前最新模型应用于对象检测的挑战，因为它们是为密集图像设计的。在这项工作中，我们在事件数据上评估了Yolo对象检测模型。为此，我们通过稀疏的卷积或异步稀疏卷积代替密集卷积层，这些卷积可以直接处理基于事件的图像，并将性能和运行时与馈送事件 - 截然图进行比较。在这里，超参数在所有变体中共享，以隔离稀疏分类对检测性能的影响。 在此，我们表明当前的稀疏卷积实现无法将其理论较低的计算要求转化为改进的运行时。
Event-based image representations are fundamentally different to traditional dense images. This poses a challenge to apply current state-of-the-art models for object detection as they are designed for dense images. In this work we evaluate the YOLO object detection model on event data. To this end we replace dense-convolution layers by either sparse convolutions or asynchronous sparse convolutions which enables direct processing of event-based images and compare the performance and runtime to feeding event-histograms into dense-convolutions. Here, hyper-parameters are shared across all variants to isolate the effect sparse-representation has on detection performance. At this, we show that current sparse-convolution implementations cannot translate their theoretical lower computation requirements into an improved runtime.
