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

Abstract:

While video-language models are essential for applications like moment retrieval and highlight detection, they frequently encounter difficulties in capturing the dynamic, non-linear interplay between temporal video sequences and textual semantics. Current methodologies, which typically depend on static cross-attention or prompt-tuning mechanisms, are unable to adaptively model the shifting relationships between different modalities. This limitation results in inadequate alignment and constrained generalization capabilities. Drawing inspiration from systems biology, we introduce Reaction-Diffusion Multimodal Fusion (RDMF), a novel framework that reconceptualizes video-language alignment through the lens of a reaction-diffusion (RD) process, utilizing the pattern formation principles established by Alan Turing. Within the RDMF architecture, video features undergo diffusion across the temporal dimension to encompass contextual information, whereas interactions between text and video are treated as non-linear reactions. These reactions serve to amplify pertinent features while filtering out noise, thereby generating emergent patterns similar to those observed in biological systems. By utilizing the Gray-Scott RD model, we have engineered a computationally efficient fusion module that combines video and text representations. This design is underpinned by a rigorous mathematical analysis of stability and convergence, applying Turing instability criteria. RDMF is theoretically robust, leveraging sophisticated mathematical tools to guarantee stable pattern formation, and is practically implementable by integrating standard elements such as pretrained encoders and DETR-style heads for tasks including moment retrieval and saliency prediction. As a pioneering interdisciplinary effort, this approach bridges the gap between systems biology and multimedia research, aiming to overcome the shortcomings of traditional multimodal fusion techniques. Initial experimental results suggest that RDMF holds the potential to surpass current methods in identifying salient video moments, presenting a fresh paradigm for video-language tasks.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC