Reconstruction as a Bridge for Event-Based Visual Question Answering

• URL: http://arxiv.org/abs/2512.11510v1
• Date: Fri, 12 Dec 2025 12:16:45 GMT
• Title: Reconstruction as a Bridge for Event-Based Visual Question Answering
• Authors: Hanyue Lou, Jiayi Zhou, Yang Zhang, Boyu Li, Yi Wang, Guangnan Ye, Boxin Shi,
• Abstract summary: We propose a straightforward Frame-based Reconstruction and Tokenization (FRT) method and an efficient Adaptive Reconstruction and Tokenization (ART) method.<n>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.<n>Our experiments demonstrate that our methods achieve state-of-the-art performance on EvQA, highlighting the significant potential of MLLMs in event-based vision.
• Score: 50.47820990163094
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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