Turing Patterns for Multimedia: Reaction-Diffusion Multi-Modal Fusion for Language-Guided Video Moment Retrieval
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMultimedia
AI summaryⓘ
The authors propose a new way for computers to understand videos and text together by using ideas from biology, specifically how patterns form in nature. Their method, called Reaction-Diffusion Multimodal Fusion (RDMF), treats video features like spreading chemicals and text-video interactions like reactions that highlight important parts and reduce noise. This approach helps the computer better link video moments with text descriptions, improving tasks like finding key moments in videos. They also use advanced math to ensure their method is stable and efficient, showing promise compared to earlier methods.
video-language modelsmoment retrievalcross-attentionreaction-diffusion systemTuring patternsGray-Scott modelmultimodal fusionpretrained encodersDETRpattern formation
Authors
Xiang Fang, Wanlong Fang, Wei Ji, Tat-Seng Chua
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.