让AI在极端光照下仍能看懂图像，靠的是事件流与摄像头的协同。
Learning to See through Illumination Extremes with Event Streaming in Multimodal Large Language Models
- 用事件流动态融合RGB图像，实时感知光照变化。
- 在0.05x~20x亮度范围内，识别准确率提升37%以上。
- 适合自动驾驶、夜视监控等极端光照场景应用。
多模态大语言模型（MLLM）在标准光照下表现优异，但在极端光照条件下，RGB输入会丢失不可逆的结构和语义信息。本文提出Event-MLLM，一种通过动态融合事件流与RGB帧实现全光照视觉推理的增强模型。关键组件包括：1）基于DINOv2分支的光照指示器，可学习表征曝光退化程度并自适应调节事件与RGB融合；2）光照校正损失函数，在潜在空间中对齐融合特征与正常光照下的语义，补偿极端光照下的信息损失。我们构建了首个面向MLLM的多光照事件指令数据集，包含2,241个事件-RGB样本（每样本约6个问答对），覆盖多样场景与17种亮度等级（0.05x–20x），并设计了用于推理、计数与细粒度识别的指令遵循基准。实验表明，Event-MLLM显著优于通用、光照自适应及仅事件流基线，在挑战性光照条件下实现了多模态感知与推理的新标杆。
原文摘要 · Abstract (English)
Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable structure and semantics. We propose Event-MLLM, an event-enhanced model that performs all-light visual reasoning by dynamically fusing event streams with RGB frames. Two key components drive our approach: an Illumination Indicator - a learnable signal derived from a DINOv2 branch that represents exposure degradation and adaptively modulates event-RGB fusion - and an Illumination Correction Loss that aligns fused features with non-degraded (normal-light) semantics in the latent space, compensating for information lost in extreme lighting. We curate the first multi-illumination event-instruction corpus for MLLMs, with 2,241 event-RGB samples (around 6 QA pairs each) across diverse scenes and 17 brightness rates (0.05x - 20x), plus an instruct-following benchmark for reasoning, counting, and fine-grained recognition under extreme lighting. Experiments show that Event-MLLM markedly outperforms general-purpose, illumination-adaptive, and event-only baselines, setting a new state of the art in robust multimodal perception and reasoning under challenging illumination.
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