mlsanityData to 7 Oct 2026

Unsupervised Semantic Parsing of Video Collections

arXiv 1506.08438 ↗First posted 28 Jun 2015cs.CVcited by 26

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Abstract (arXiv)

Human communication typically has an underlying structure. This is reflected in the fact that in many user generated videos, a starting point, ending, and certain objective steps between these two can be identified. In this paper, we propose a method for parsing a video into such semantic steps in an unsupervised way. The proposed method is capable of providing a semantic "storyline" of the video composed of its objective steps. We accomplish this using both visual and language cues in a joint generative model. The proposed method can also provide a textual description for each of the identified semantic steps and video segments. We evaluate this method on a large number of complex YouTube videos and show results of unprecedented quality for this intricate and impactful problem.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2015.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 6; 2024: 1; 2025: 1; 2026: 4.

26 papers.

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  1. 2021MERLOT: Multimodal Neural Script Knowledge Modelscited by 311
  2. 2021Broaden Your Views for Self-Supervised Video Learningcited by 108
  3. 2022Temporal Action Segmentation: An Analysis of Modern Techniquescited by 85
  4. 2022My View is the Best View: Procedure Learning from Egocentric Videoscited by 81
  5. 2021Unsupervised Action Segmentation by Joint Representation Learning and Online Clusteringcited by 48
  6. 2023Learning Procedure-aware Video Representation from Instructional Videos and Their Narrationscited by 46
  7. 2022TL;DW? Summarizing Instructional Videos with Task Relevance & Cross-Modal Saliencycited by 35
  8. 2023Video-Mined Task Graphs for Keystep Recognition in Instructional Videoscited by 35
  9. 2023Learning to Ground Instructional Articles in Videos through Narrationscited by 28
  10. 2021Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activitiescited by 26
  11. 2022Timestamp-Supervised Action Segmentation with Graph Convolutional Networkscited by 18
  12. 2023Permutation-Aware Action Segmentation via Unsupervised Frame-to-Segment Alignmentcited by 17
14 more
  1. 2021Temporal Action Segmentation with High-level Complex Activity Labelscited by 16
  2. 2021SVIP: Sequence VerIfication for Procedures in Videoscited by 16
  3. 2021Weakly-Supervised Video Object Grounding via Causal Interventioncited by 13
  4. 2023United We Stand, Divided We Fall: UnityGraph for Unsupervised Procedure Learning from Videoscited by 13
  5. 2023OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentationcited by 11
  6. 2021Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actionscited by 10
  7. 2025Building Egocentric Procedural AI Assistant: Methods, Benchmarks, and Challengescited by 5
  8. 2024Learning Multiple Object States from Actions via Large Language Modelscited by 4
  9. 2021Unsupervised Discovery of Actions in Instructional Videoscited by 2
  10. 2022Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Methodcited by 2
  11. 2026Hierarchical Action Learning for Weakly-Supervised Action Segmentationcited by 1
  12. 2026Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantizationcited by 0
  13. 2026Jointly Learning Structured Representations and Stabilized Affinity for Human Motion Segmentationcited by 0
  14. 2026Improving Temporal Action Segmentation via Constraint-Aware Decodingcited by 0