LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework

• URL: http://arxiv.org/abs/2502.14273v1
• Date: Thu, 20 Feb 2025 05:18:36 GMT
• Title: LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework
• Authors: Zongyou Yu, Qiang Qu, Qian Zhang, Nan Zhang, Xiaoming Chen,
• Abstract summary: Large language models (LLMs) have exhibited remarkable zero-shot capabilities across diverse domains.<n>We propose textbfLLM-EvGen, an event representation generator that produces event representations textbfLLM-EvRep<n> Comprehensive experiments were conducted on three datasets: N-ImageNet, N-Caltech101, and N-MNIST.
• Score: 11.30784253260618
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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