Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

@article{Hu2016BottomUpAT,
  title={Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians},
  author={Peiyun Hu and Deva Ramanan},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={5600-5609}
}
Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, practical experience and biological evidence tells us that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work explores "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by… CONTINUE READING
Highly Cited
This paper has 43 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 12 times over the past 90 days. VIEW TWEETS
32 Citations
59 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 32 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 59 references

Similar Papers

Loading similar papers…