Recent advancements in event-based recognition have demonstrated significant promise, yet most existing approaches rely on extensive training, limiting their adaptability for efficient processing of event-driven visual content. Meanwhile, large language models (LLMs) have exhibited remarkable zero-shot capabilities across diverse domains, but their application to event-based visual recognition remains largely unexplored. To bridge this gap, we propose \textbf{LLM-EvGen}, an event representation generator that produces LLM-compatible event representations \textbf{LLM-EvRep}, thereby enhancing the performance of LLMs on event recognition tasks. The generator is trained using a self-supervised framework, aligning the generated representations with semantic consistency and structural fidelity. Comprehensive experiments were conducted on three datasets: N-ImageNet, N-Caltech101, and N-MNIST. The results demonstrate that our method, \textbf{LLM-EvRep}, outperforms the event-to-video method, E2VID, by 15.93\%, 0.82\%, and 50.21\%, respectively, in recognition tasks when evaluated using GPT-4o.

本研究解决了事件驱动视觉内容处理效率低下的问题，提出了LLM-EvGen，一个生成与大型语言模型（LLM）兼容的事件表示的生成器LLM-EvRep。通过自监督框架进行训练，该方法在三种数据集上的实验结果显示，LLM-EvRep在事件识别任务中相对于E2VID方法的性能提升显著，展示了其潜在的广泛应用价值。

LLM-EvRep：使用自监督框架学习与LLM兼容的事件表示