Event cameras are a type of novel neuromorphic sen-sor that has been gaining increasing attention. Existing event-based backbones mainly rely on image-based designs to extract spatial information within the image transformed from events, overlooking important event properties like time and polarity. To address this issue, we propose a novel Group-based vision Transformer backbone for Event-based vision, called Group Event Transformer (GET), which de-couples temporal-polarity information from spatial infor-mation throughout the feature extraction process. Specifi-cally, we first propose a new event representation for GET, named Group Token, which groups asynchronous events based on their timestamps and polarities. Then, GET ap-plies the Event Dual Self-Attention block, and Group Token Aggregation module to facilitate effective feature commu-nication and integration in both the spatial and temporal-polarity domains. After that, GET can be integrated with different downstream tasks by connecting it with vari-ous heads. We evaluate our method on four event-based classification datasets (Cifar10-DVS, N-MNIST, N-CARS, and DVS128Gesture) and two event-based object detection datasets (1Mpx and Gen1), and the results demonstrate that GET outperforms other state-of-the-art methods. The code is available at https://github.com/Peterande/GET-Group-Event-Transformer.

提出了一种新颖的基于团体的神经元感知传感器，名为Group Event Transformer（GET），用于事件驱动视觉的特征提取，该方法解耦了特征提取过程中的时间极性信息和空间信息，并通过事件双自注意机制和团体令牌聚合模块在时间极性和空间领域内进行有效的特征通信和整合。在四个事件驱动分类数据集和两个事件驱动目标检测数据集上的实验证明，GET优于现有的其他方法。

GET: 组合事件变换器对于基于事件的视觉