ActivityNet: A large-scale video benchmark for human activity understanding

@article{Heilbron2015ActivityNetAL,
  title={ActivityNet: A large-scale video benchmark for human activity understanding},
  author={Fabian Caba Heilbron and Victor Escorcia and Bernard Ghanem and Juan Carlos Niebles},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={961-970}
}
In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper we introduce ActivityNet, a new large-scale video benchmark for human activity understanding. Our benchmark aims at covering a… CONTINUE READING
Highly Influential
This paper has highly influenced 60 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 374 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
243 Extracted Citations
37 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 243 extracted citations

374 Citations

0501001502015201620172018
Citations per Year
Semantic Scholar estimates that this publication has 374 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 37 references

A dataset of 101 human action classes from videos in the wild

  • A.R.Z. Khurram Soomro, M. Shah
  • Technical report, University of Central Florida,
  • 2012
Highly Influential
4 Excerpts

Similar Papers

Loading similar papers…