UntrimmedNets for Weakly Supervised Action Recognition and Detection

@article{Wang2017UntrimmedNetsFW,
  title={UntrimmedNets for Weakly Supervised Action Recognition and Detection},
  author={Limin Wang and Yuanjun Xiong and Dahua Lin and Luc Van Gool},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={6402-6411}
}
Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the… CONTINUE READING
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