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arXiv · 2512.11510

Reconstruction as a Bridge for Event-Based Visual Question Answering

Hanyue Lou·Jiayi Zhou·Yang Zhang·Boyu Li·Yi Wang·Guangnan Ye·Boxin Shi
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Abstract

Integrating event cameras with Multimodal Large Language Models (MLLMs) promises general scene understanding in challenging visual conditions, yet requires navigating a trade-off between preserving the unique advantages of event data and ensuring compatibility with frame-based models. We address this challenge by using reconstruction as a bridge, proposing a straightforward Frame-based Reconstruction and Tokenization (FRT) method and designing an efficient Adaptive Reconstruction and Tokenization (ART) method that leverages event sparsity. For robust evaluation, we introduce EvQA, the first objective, real-world benchmark for event-based MLLMs, comprising 1,000 event-Q&A pairs from 22 public datasets. Our experiments demonstrate that our methods achieve state-of-the-art performance on EvQA, highlighting the significant potential of MLLMs in event-based vision.

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Hanyue Lou, Jiayi Zhou, Yang Zhang, Boyu Li, Yi Wang, Guangnan Ye, Boxin Shi. 2025-12-12. Reconstruction as a Bridge for Event-Based Visual Question Answering. https://arxiv.org/abs/2512.11510

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