Corpus ID: 128301287

Tripping through time: Efficient Localization of Activities in Videos

@article{Hahn2020TrippingTT,
  title={Tripping through time: Efficient Localization of Activities in Videos},
  author={Meera Hahn and Asim Kadav and James M. Rehg and H. Graf},
  journal={ArXiv},
  year={2020},
  volume={abs/1904.09936}
}
  • Meera Hahn, Asim Kadav, +1 author H. Graf
  • Published 2020
  • Computer Science
  • ArXiv
  • Localizing moments in untrimmed videos via language queries is a new and interesting task that requires the ability to accurately ground language into video. [...] Key Method Furthermore, TripNet uses reinforcement learning to efficiently localize relevant activity clips in long videos, by learning how to intelligently skip around the video. It extracts visual features for fewer frames to perform activity classification. In our evaluation over Charades-STA, ActivityNet Captions and the TACoS dataset, we find…Expand Abstract
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