Semantic-Guided Unsupervised Video Summarization

2026-01-21

AI Summary

This work proposes a semantic-guided unsupervised video summarization method to address the limitations of existing approaches, which often rely on single-modality features, lack semantic guidance, and suffer from unstable GAN training. The proposed framework employs a frame-level semantic alignment attention mechanism to guide a Transformer-based generator in reconstructing keyframes, effectively integrating high-level semantic information to enhance keyframe selection quality. Furthermore, a progressive training strategy is introduced to stabilize GAN optimization. Extensive experiments on multiple benchmark datasets demonstrate that the proposed method outperforms current unsupervised video summarization techniques in terms of summary quality and fidelity.

Technology Category

• Natural Language Processing: Summarization
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Unsupervised & Self-Supervised Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Video summarization is a crucial technique for social understanding, enabling efficient browsing of massive multimedia content and extraction of key information from social platforms. Most existing unsupervised summarization methods rely on Generative Adversarial Networks (GANs) to enhance keyframe selection and generate coherent, video summaries through adversarial training. However, such approaches primarily exploit unimodal features, overlooking the guiding role of semantic information in keyframe selection, and often suffer from unstable training. To address these limitations, we propose a novel Semantic-Guided Unsupervised Video Summarization method. Specifically, we design a novel frame-level semantic alignment attention mechanism and integrate it into a keyframe selector, which guides the Transformer-based generator within the adversarial framework to better reconstruct videos. In addition, we adopt an incremental training strategy to progressively update the model components, effectively mitigating the instability of GAN training. Experimental results demonstrate that our approach achieves superior performance on multiple benchmark datasets.

Problem

• video summarization
• unsupervised learning
• semantic guidance
• keyframe selection
• GAN instability

Innovation

• Semantic-Guided
• Unsupervised Video Summarization
• Frame-level Semantic Alignment
• Incremental Training
• Transformer-based Generator