mlsanityData to 7 Oct 2026

Discerning Generic Event Boundaries in Long-Form Wild Videos

arXiv 2106.10090 ↗First posted 18 Jun 2021cs.CV cs.AIcited by 6

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Abstract (arXiv)

Detecting generic, taxonomy-free event boundaries invideos represents a major stride forward towards holisticvideo understanding. In this paper we present a technique forgeneric event boundary detection based on a two stream in-flated 3D convolutions architecture, which can learn spatio-temporal features from videos. Our work is inspired from theGeneric Event Boundary Detection Challenge (part of CVPR2021 Long Form Video Understanding- LOVEU Workshop).Throughout the paper we provide an in-depth analysis ofthe experiments performed along with an interpretation ofthe results obtained.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 20 references that are in the map. First posted on arXiv 18 Jun 2021.

Builds on

10 of 20 references.

References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
  3. 2016The THUMOS Challenge on Action Recognition for Videos "in the Wild"cited by 213
  4. 2018Rethinking the Faster R-CNN Architecture for Temporal Action Localizationcited by 194
  5. 2018When will you do what? - Anticipating Temporal Occurrences of Activitiescited by 106
  6. 2016Segmental Spatiotemporal CNNs for Fine-grained Action Segmentationcited by 78
  7. 2021Generic Event Boundary Detection: A Benchmark for Event Segmentationcited by 64
  8. 2020Intra- and Inter-Action Understanding via Temporal Action Parsingcited by 41
  9. 2017Cascaded Boundary Regression for Temporal Action Detectioncited by 35
  10. 2019TransNet: A deep network for fast detection of common shot transitionscited by 15

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 0; 2025: 1.

6 papers.