Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

  title={Interpretable 3D Human Action Analysis with Temporal Convolutional Networks},
  author={Tae Soo Kim and Austin Reiter},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action representation with 3D skeletons, the quality and the pace of recent progress have been significant. However, the inner workings of state-of-the-art learning based methods in 3D human action recognition still remain mostly black-box. In this work, we propose to use a new class of models known as Temporal Convolutional… CONTINUE READING
Highly Cited
This paper has 28 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 28 times over the past 90 days. VIEW TWEETS
21 Citations
39 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 21 extracted citations


Publications referenced by this paper.
Showing 1-10 of 39 references

Similar Papers

Loading similar papers…