Memory-Efficient Graph Convolutional Networks for Object Classification
  and Detection with Event Cameras
Kamil Jeziorek Andrea Pinna Tomasz Kryjak
Recent advances in event camera research emphasize processing data in its
original sparse form, which allows the use of its unique features such as high
temporal resolution, high dynamic range, low latency, and resistance to image
blur. One promising approach for analyzing event data is through graph
convolutional networks (GCNs). However, current research in this domain
primarily focuses on optimizing computational costs, neglecting the associated
memory costs. In this paper, we consider both factors together in order to
achieve satisfying results and relatively low model complexity. For this
purpose, we performed a comparative analysis of different graph convolution
operations, considering factors such as execution time, the number of trainable
model parameters, data format requirements, and training outcomes. Our results
show a 450-fold reduction in the number of parameters for the feature
extraction module and a 4.5-fold reduction in the size of the data
representation while maintaining a classification accuracy of 52.3%, which is
6.3% higher compared to the operation used in state-of-the-art approaches. To
further evaluate performance, we implemented the object detection architecture
and evaluated its performance on the N-Caltech101 dataset. The results showed
an accuracy of 53.7 % mAP@0.5 and reached an execution rate of 82 graphs per
second.