Learning Bottleneck Transformer for Event Image-Voxel Feature Fusion
  based Classification
Chengguo Yuan Yu Jin Zongzhen Wu Fanting Wei Yangzirui Wang Lan Chen Xiao Wang
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:
\url{https://github.com/Event-AHU/EFV_event_classification}