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Semantic-Guided Unsupervised Video Summarization

Haizhou Liu, Haodong Jin, Yiming Wang, Hui Yu

TL;DR

The paper tackles unsupervised video summarization by incorporating semantic information into frame selection through a frame-level semantic-alignment attention mechanism. It leverages cross-modal cosine similarity between visual and semantic frame representations extracted via a CLIP encoder to guide a Transformer-based generator within an adversarial framework, with an incremental training strategy to stabilize GAN optimization. The approach achieves state-of-the-art performance on SumMe and TVSum, with ablations confirming the contribution of semantic guidance and cross-modal interactions to summary quality. This work advances unsupervised video summarization by integrating semantic cues to improve representativeness and coherence while addressing training instability, with potential extensions to audio-visual modalities.

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.

Semantic-Guided Unsupervised Video Summarization

TL;DR

The paper tackles unsupervised video summarization by incorporating semantic information into frame selection through a frame-level semantic-alignment attention mechanism. It leverages cross-modal cosine similarity between visual and semantic frame representations extracted via a CLIP encoder to guide a Transformer-based generator within an adversarial framework, with an incremental training strategy to stabilize GAN optimization. The approach achieves state-of-the-art performance on SumMe and TVSum, with ablations confirming the contribution of semantic guidance and cross-modal interactions to summary quality. This work advances unsupervised video summarization by integrating semantic cues to improve representativeness and coherence while addressing training instability, with potential extensions to audio-visual modalities.

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.
Paper Structure (13 sections, 5 equations, 2 figures, 3 tables)

This paper contains 13 sections, 5 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Unsupervised Video Summarization
  4. Proposed Method
  5. Overview of Technical Framework
  6. Frame Selector with Semantic-alignment Attention
  7. Unsupervised Learning Strategy
  8. Experiments
  9. Datasets and Experimental Settings
  10. Quantitative Comparison
  11. Qualitative Comparison
  12. Ablation Study
  13. Conclusion and Future Work

Figures (2)

  • Figure 1: Overview workflow of the proposed framework for unsupervised video summarization. Symbols: $\oplus$ represents Concatenation, $\otimes$ represents Element-wise multi.
  • Figure 2: Partial summary visualization results generated by the proposed method. Gray is ground truth, blue is the summary result of model prediction, and the height of the column indicates the importance of the frame.The video summary on the left is from the first video in the SumMe dataset, while the one on the right is from the 15th video in the TVSum dataset.