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Video Summarization Using Fully Convolutional Sequence Networks

arXiv 1805.10538 ↗First posted 26 May 2018cs.CVcited by 71

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Abstract (arXiv)

This paper addresses the problem of video summarization. Given an input video, the goal is to select a subset of the frames to create a summary video that optimally captures the important information of the input video. With the large amount of videos available online, video summarization provides a useful tool that assists video search, retrieval, browsing, etc. In this paper, we formulate video summarization as a sequence labeling problem. Unlike existing approaches that use recurrent models, we propose fully convolutional sequence models to solve video summarization. We firstly establish a novel connection between semantic segmentation and video summarization, and then adapt popular semantic segmentation networks for video summarization. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of our models.

Key facts. Cited by 71 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2018.

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Citing papers per year: 2021: 17; 2022: 12; 2023: 16; 2024: 12; 2025: 12; 2026: 2.

71 papers.

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  1. 2023Query-Dependent Video Representation for Moment Retrieval and Highlight Detectioncited by 166
  2. 2021CLIP-It! Language-Guided Video Summarizationcited by 99
  3. 2021Video Summarization Using Deep Neural Networks: A Surveycited by 85
  4. 2024Frame-Voyager: Learning to Query Frames for Video Large Language Modelscited by 83
  5. 2023Align and Attend: Multimodal Summarization with Dual Contrastive Lossescited by 65
  6. 2021Deep Reinforcement Learning in Computer Vision: A Comprehensive Surveycited by 43
  7. 2023VideoXum: Cross-modal Visual and Textural Summarization of Videoscited by 41
  8. 2024AMEGO: Active Memory from long EGOcentric videoscited by 36
  9. 2022TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliencycited by 35
  10. 2021Reconstructive Sequence-Graph Network for Video Summarizationcited by 34
  11. 2023Correlation-Guided Query-Dependency Calibration for Video Temporal Groundingcited by 31
  12. 2021Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Netcited by 22
38 more
  1. 2022Progressive Video Summarization via Multimodal Self-supervised Learningcited by 21
  2. 2024Saliency-Guided DETR for Moment Retrieval and Highlight Detectioncited by 21
  3. 2021Hierarchical Multimodal Transformer to Summarize Videoscited by 19
  4. 2021Learning Triadic Belief Dynamics in Nonverbal Communication from Videoscited by 17
  5. 2021UnweaveNet: Unweaving Activity Storiescited by 14
  6. 2023MMSum: A Dataset for Multimodal Summarization and Thumbnail Generation of Videoscited by 12
  7. 2021Video Joint Modelling Based on Hierarchical Transformer for Co-summarizationcited by 10
  8. 2024Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Groundingcited by 10
  9. 2025MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learningcited by 10
  10. 2023Learning Segment Similarity and Alignment in Large-Scale Content Based Video Retrievalcited by 9
  11. 2024Towards Automated Movie Trailer Generationcited by 9
  12. 2021DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarizationcited by 8
  13. 2023Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understandingcited by 7
  14. 2021PR-Net: Preference Reasoning for Personalized Video Highlight Detectioncited by 6
  15. 2022Video Summarization Overviewcited by 6
  16. 2023Conditional Modeling Based Automatic Video Summarizationcited by 6
  17. 2024Cluster-based Video Summarization with Temporal Context Awarenesscited by 6
  18. 2024Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrencecited by 6
  19. 2025Memory Storyboard: Leveraging Temporal Segmentation for Streaming Self-Supervised Learning from Egocentric Videoscited by 6
  20. 2021AudioVisual Video Summarizationcited by 5
  21. 2022Contrastive Losses Are Natural Criteria for Unsupervised Video Summarizationcited by 5
  22. 2023VVS: Video-to-Video Retrieval with Irrelevant Frame Suppressioncited by 5
  23. 2025Spiking Variational Graph Representation Inference for Video Summarizationcited by 5
  24. 2022Multimodal Frame-Scoring Transformer for Video Summarizationcited by 4
  25. 2021Use of Affective Visual Information for Summarization of Human-Centric Videoscited by 3
  26. 2021ERA: Entity Relationship Aware Video Summarization with Wasserstein GANcited by 3
  27. 2021HighlightMe: Detecting Highlights from Human-Centric Videoscited by 3
  28. 2025Prompts to Summaries: Zero-Shot Language-Guided Video Summarization with Large Language and Video Modelscited by 3
  29. 2025Language-Guided Graph Representation Learning for Video Summarizationcited by 3
  30. 2022MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarizationcited by 2
  31. 2022Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attentioncited by 2
  32. 2023Masked Autoencoder for Unsupervised Video Summarizationcited by 2
  33. 2023Does Video Summarization Require Videos? Quantifying the Effectiveness of Language in Video Summarizationcited by 2
  34. 2025Weakly Supervised Temporal Sentence Grounding via Positive Sample Miningcited by 2
  35. 2025TRIM: A Self-Supervised Video Summarization Framework Maximizing Temporal Relative Information and Representativenesscited by 2
  36. 2026MMViR: A Multi-Modal and Multi-Granularity Representation for Long-range Video Understandingcited by 2
  37. 2022Exploring Global Diversity and Local Context for Video Summarizationcited by 1
  38. 2022A Multi-stage deep architecture for summary generation of soccer videoscited by 1

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