Recognizing target objects using an event-based camera draws more and more
attention in recent years. Existing works usually represent the event streams
into point-cloud, voxel, image, etc, and learn the feature representations
using various deep neural networks. Their final results may be limited by the
following factors: monotonous modal expressions and the design of the network
structure. To address the aforementioned challenges, this paper proposes a
novel dual-stream framework for event representation, extraction, and fusion.
This framework simultaneously models two common representations: event images
and event voxels. By utilizing Transformer and Structured Graph Neural Network
(GNN) architectures, spatial information and three-dimensional stereo
information can be learned separately. Additionally, a bottleneck Transformer
is introduced to facilitate the fusion of the dual-stream information.
Extensive experiments demonstrate that our proposed framework achieves
state-of-the-art performance on two widely used event-based classification
datasets. The source code of this work is available at:
https://github.com/Event-AHU/EFV_event_classification

该研究提出了一个新颖的双流框架，用于事件表示、提取和融合，通过利用 Transformer 和结构化图神经网络（GNN）架构，分别可以学习空间信息和三维立体信息，并引入瓶颈 Transformer 以促进双流信息的融合，通过广泛实验证明了该框架在两个广泛使用的事件分类数据集上取得了最先进的性能。