Video-Text Temporal Localization via Multi-Scale Convolution and Dynamic Routing

Video-text temporal localization requires precise alignment between natural language queries and corresponding video segments, a fundamental challenge in multimodal understanding. We present a novel framework that addresses two critical limitations of existing methods: inadequate modeling of hierarchical temporal structure and inability to handle complex many-to-many correspondences between modalities. Our approach introduces a multi-scale temporal convolutional encoder that captures motion patterns across different temporal granularities - from instantaneous frame transitions to extended action sequences. We further propose a capsule-based dynamic routing mechanism that iteratively refines segment-query associations through structured agreement updates, enabling flexible modeling of non-monotonic alignments. These components are unified through a multi-task learning objective that jointly optimizes temporal boundary regression, cross-modal semantic alignment, and capsule diversity. Extensive experiments on ActivityNet Captions demonstrate significant improvements, achieving 42.9% Recall@0.5 and 41.1% mean IoU, surpassing strong transformer-based baselines while maintaining computational efficiency. Our results validate that combining hierarchical temporal modeling with structured semantic routing provides an effective solution for fine-grained video-language understanding.

Semantic Video Communication via Multi-Scale Convolution and Dynamic Routing for Next-Generation Networks

The exponential growth of video traffic demands novel semantic communication paradigms that transmit meaning rather than raw bits. We present a generative AI-enabled framework for semantic video communication addressing two critical challenges: efficient hierarchical temporal modeling for bandwidth-constrained transmission and robust semantic alignment between video content and natural language queries at network edge devices. Our approach introduces a multi-scale temporal convolutional encoder that captures motion patterns across different temporal granularities with O(T) complexity suitable for resource-constrained IoT deployments. We further propose a capsule-based dynamic routing mechanism that iteratively refines segment-query associations, enabling flexible modeling of non-monotonic semantic alignments essential for goal-oriented communication. These components are unified through a multi-task learning objective optimizing temporal boundary regression, cross-modal alignment, and capsule diversity. Experiments on ActivityNet Captions demonstrate significant improvements, achieving 42.9% Recall@0.5 and 41.1% mean IoU while maintaining computational efficiency critical for edge deployment.