Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network

@article{Shi2017LearningLD,
  title={Learning Long-Term Dependencies for Action Recognition with a Biologically-Inspired Deep Network},
  author={Yemin Shi and Yonghong Tian and Yaowei Wang and Wei Zeng and Tiejun Huang},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={716-725}
}
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task. As one of the key models for sequence learning, recurrent neural network (RNN) and its variants such as long short term memory (LSTM) and gated recurrent unit (GRU) are still not powerful enough in practice. One possible reason is that they have only feedforward connections, which is different from the biological neural system that is… 

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