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

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Internet of Things (IoT) & Connected Devices, Robotics & Autonomous Systems · Depth: Advanced, extended

Summary

A novel generative AI-enabled framework for semantic video communication addresses challenges in next-generation networks by focusing on transmitting meaning rather than raw bits. This framework introduces a multi-scale temporal convolutional encoder, which captures motion patterns across different temporal granularities with O(T) complexity, making it suitable for resource-constrained IoT deployments. It also incorporates a capsule-based dynamic routing mechanism that iteratively refines segment–query associations, enabling flexible modeling of non-monotonic semantic alignments. These innovations are unified through a multi-task learning objective that optimizes temporal boundary regression, cross-modal alignment, and capsule diversity. Experiments on ActivityNet Captions demonstrated significant improvements, achieving 42.9% Recall@0.5 and 41.1% mean IoU, while maintaining computational efficiency critical for edge deployment. The use of a BERT-base text encoder further boosted performance, highlighting the importance of robust linguistic understanding.

Key takeaway

For AI Engineers developing semantic video communication systems for bandwidth-constrained edge devices, you should integrate multi-scale temporal convolutional encoders to achieve O(T) complexity. Prioritize capsule-based dynamic routing for robust, interpretable video-text alignment, especially for goal-oriented queries. Your choice of text encoder, like BERT-base, is crucial for performance, potentially yielding greater gains than scaling visual backbones. Consider soft supervision for ground truth boundaries to handle annotation ambiguities effectively.

Key insights

Semantic video communication for next-gen networks requires efficient multi-scale temporal modeling and structured, dynamic video-text alignment.

Principles

• Hierarchical temporal modeling improves video understanding.
• Structured semantic alignment enhances goal-oriented communication.
• Text encoder quality is critical for semantic query understanding.

Method

The framework uses a multi-scale convolutional encoder for O(T) temporal modeling and a capsule-based dynamic routing module for iterative video-text alignment. A multi-task loss optimizes boundary prediction, cross-modal alignment, and capsule diversity.

In practice

• Implement multi-scale 1D convolutions for O(T) temporal processing.
• Use capsule networks for flexible, many-to-many video-text alignment.
• Prioritize robust text encoders like BERT-base for semantic queries.

Topics

• Semantic Communication
• Video-Text Localization
• Multi-Scale Convolution
• Dynamic Routing
• Edge AI
• ActivityNet Captions

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.