# Exploring global diverse attention via pairwise temporal relation for video summarization

arXiv: https://arxiv.org/abs/2009.10942 · HTML: https://mlsanity.com/paper/2009.10942/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2020.

## Built on by (most cited first, 29 of 29)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [HiTeA: Hierarchical Temporal-Aware Video-Language Pre-training](https://mlsanity.com/paper/2212.14546/) (2022)
- [A Review of Deep Learning for Video Captioning](https://mlsanity.com/paper/2304.11431/) (2023)
- [CSTA: CNN-based Spatiotemporal Attention for Video Summarization](https://mlsanity.com/paper/2405.11905/) (2024)
- [Hierarchical Multimodal Transformer to Summarize Videos](https://mlsanity.com/paper/2109.10559/) (2021)
- [Cluster-based Video Summarization with Temporal Context Awareness](https://mlsanity.com/paper/2404.04511/) (2024)
- [VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression](https://mlsanity.com/paper/2303.08906/) (2023)
- [SummDiff: Generative Modeling of Video Summarization with Diffusion](https://mlsanity.com/paper/2510.08458/) (2025)
- [Use of Affective Visual Information for Summarization of Human-Centric Videos](https://mlsanity.com/paper/2107.03783/) (2021)
- [SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization](https://mlsanity.com/paper/2509.01439/) (2025)
- [MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization](https://mlsanity.com/paper/2204.08352/) (2022)
- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)
- [Masked Autoencoder for Unsupervised Video Summarization](https://mlsanity.com/paper/2306.01395/) (2023)
- [TriPSS: A Tri-Modal Keyframe Extraction Framework Using Perceptual, Structural, and Semantic Representations](https://mlsanity.com/paper/2506.05395/) (2025)
- [TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativeness](https://mlsanity.com/paper/2506.20588/) (2025)
- [Exploring Global Diversity and Local Context for Video Summarization](https://mlsanity.com/paper/2201.11345/) (2022)
- [Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames](https://mlsanity.com/paper/2311.17940/) (2023)
- [Facilitating the Production of Well-tailored Video Summaries for Sharing on Social Media](https://mlsanity.com/paper/2312.02616/) (2023)
- [An Integrated Framework for Multi-Granular Explanation of Video Summarization](https://mlsanity.com/paper/2405.10082/) (2024)
- [Video Summarization Techniques: A Comprehensive Review](https://mlsanity.com/paper/2410.04449/) (2024)
- [From Captions to Keyframes: KeyScore for Multimodal Frame Scoring and Video-Language Understanding](https://mlsanity.com/paper/2510.06509/) (2025)
- [TRIMMER: A New Paradigm for Video Summarization through Self-Supervised Reinforcement Learning](https://mlsanity.com/paper/2605.01659/) (2026)
- [Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network](https://mlsanity.com/paper/2105.11131/) (2021)
- [SELF-VS: Self-supervised Encoding Learning For Video Summarization](https://mlsanity.com/paper/2303.15993/) (2023)
- [Learning Subjective Time-Series Data via Utopia Label Distribution Approximation](https://mlsanity.com/paper/2307.07682/) (2023)
- [Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas](https://mlsanity.com/paper/2401.16076/) (2024)
- [Does SpatioTemporal information benefit Two video summarization benchmarks?](https://mlsanity.com/paper/2410.03323/) (2024)
- [FullTransNet: Full Transformer with Local-Global Attention for Video Summarization](https://mlsanity.com/paper/2501.00882/) (2025)
- [brat: Aligned Multi-View Embeddings for Brain MRI Analysis](https://mlsanity.com/paper/2512.18679/) (2025)

## Abstract (arXiv)

Video summarization is an effective way to facilitate video searching and browsing. Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to explicitly diversify the system-generated summary frames while requiring intensive computations. In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames. Particularly, the GDA module has two advantages: 1) it models the relations within paired frames as well as the relations among all pairs, thus capturing the global attention across all frames of one video; 2) it reflects the importance of each frame to the whole video, leading to diverse attention on these frames. Thus, SUM-GDA is beneficial for generating diverse frames to form satisfactory video summary. Extensive experiments on three data sets, i.e., SumMe, TVSum, and VTW, have demonstrated that SUM-GDA and its extension outperform other competing state-of-the-art methods with remarkable improvements. In addition, the proposed models can be run in parallel with significantly less computational costs, which helps the deployment in highly demanding applications.