Video-language models are pivotal for tasks such as moment retrieval and highlight detection, yet they often struggle to capture the dynamic, non-linear interactions between temporal video sequences and textual semantics. Existing approaches, relying on static cross-attention or prompt-tuning mechanisms, fail to adaptively model the evolving relationships between modalities, leading to suboptimal alignment and limited generalization. Inspired by systems biology, we propose \textbf{Reaction-Diffusion Multimodal Fusion (RDMF)}, a novel framework that reimagines video-language alignment as a reaction-diffusion (RD) process, drawing on the principles of pattern formation introduced by Alan Turing. In RDMF, video features diffuse across time to capture temporal context, while text-video interactions are modeled as non-linear reactions that amplify relevant features and suppress noise, forming emergent patterns akin to biological systems. Leveraging the Gray-Scott RD model, we design a computationally efficient fusion module that integrates video and text representations, supported by rigorous mathematical analysis of stability and convergence using Turing instability criteria. Our framework is theoretically grounded, employing advanced mathematical tools to ensure stable pattern formation, and is practically viable, incorporating standard components like pretrained encoders and DETR-style heads for moment retrieval and saliency prediction. RDMF represents a pioneering interdisciplinary approach, bridging systems biology and multimedia research to address the limitations of conventional multimodal fusion. Preliminary experiments demonstrate its potential to outperform existing methods in identifying salient video moments, offering a new paradigm for video-language tasks.

翻译：视频-语言模型在时刻检索与精彩片段检测等任务中至关重要，但通常难以捕捉视频时序序列与文本语义之间的动态、非线性交互。现有方法依赖静态交叉注意力或提示微调机制，无法自适应地建模模态间不断演化的关系，导致对齐不佳且泛化能力受限。受系统生物学启发，我们提出**反应-扩散多模态融合（RDMF）**，这一新颖框架将视频-语言对齐重新构思为反应-扩散过程，借鉴了艾伦·图灵提出的斑图形成原理。在RDMF中，视频特征随时间扩散以捕捉时序上下文，而文本-视频交互则被建模为非线性反应，该反应放大相关特征并抑制噪声，形成类似于生物系统的涌现斑图。利用格雷-斯科特反应-扩散模型，我们设计了一个计算高效的融合模块，整合视频与文本表示，并借助图灵不稳定性判据对稳定性与收敛性进行严谨的数学分析支撑。我们的框架在理论上根基扎实，运用高级数学工具确保稳定的斑图形成，且具备实际可行性，集成了预训练编码器与面向时刻检索和显著性预测的DETR式头部等标准组件。RDMF代表了一种开创性的跨学科方法，连接系统生物学与多媒体研究，以解决传统多模态融合的局限性。初步实验表明，其在识别显著视频时刻方面具有超越现有方法的潜力，为视频-语言任务提供了新范式。