Turing Patterns for Multimedia: Reaction-Diffusion Multi-Modal Fusion for Language-Guided Video Moment Retrieval

2026-05-31

AI Summary

Existing video-language models struggle to capture the nonlinear, adaptive interactions between temporal dynamics in videos and semantic content in text, leading to suboptimal cross-modal alignment. Inspired by systems biology, this work introduces the Turing reaction-diffusion mechanism into multimodal fusion for the first time, proposing a dynamic alignment framework based on the Gray-Scott model. The framework leverages diffusion processes to model temporal context in videos and employs nonlinear reactions to amplify relevant features while suppressing noise, thereby generating Turing-pattern-like fused representations. Drawing on Turing instability theory, we theoretically analyze the stability and convergence of the fusion module. Extensive experiments on language-guided video moment retrieval demonstrate the superiority of our approach over state-of-the-art methods, achieving precise localization of key video moments.

Abstract

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.

Problem

• video-language alignment
• multimodal fusion
• temporal dynamics
• non-linear interactions
• moment retrieval

Innovation

• Reaction-Diffusion
• Turing Patterns
• Multimodal Fusion
• Video-Language Alignment
• Gray-Scott Model