Magic-wall

Author:

Liu Ting1,Wei Yunchao2,Zhao Yao1,Liu Si3,Wei Shikui1

Affiliation:

1. Beijing Jiaotong University, Beijing, China

2. National University of Singapore, Singapore, Singapore

3. Chinese Academy of Sciences, Beijing, China

Funder

National Key Research and Development of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Joint Fund of Ministry of Education of China and China Mobile

Publisher

ACM

Reference41 articles.

1. K. Alex I. Sutskever and G.E. Hinton ImageNet Classification with Deep Convolutional Neural Networks. NIPS. K. Alex I. Sutskever and G.E. Hinton ImageNet Classification with Deep Convolutional Neural Networks. NIPS.

2. M. Arun and L. Svetlana. 2015. Learning informative edge maps for indoor scene layout prediction ICCV. 936--944. 10.1109/ICCV.2015.113 M. Arun and L. Svetlana. 2015. Learning informative edge maps for indoor scene layout prediction ICCV. 936--944. 10.1109/ICCV.2015.113

3. S. Bell P. Upchurch N. Snavely and K. Bala. 2013. OpenSurfaces: A Richly Annotated Catalog of Surface Appearance. ACM Trans. Graph. (2013) 111:1--111:17. 10.1145/2461912.2462002 S. Bell P. Upchurch N. Snavely and K. Bala. 2013. OpenSurfaces: A Richly Annotated Catalog of Surface Appearance. ACM Trans. Graph. (2013) 111:1--111:17. 10.1145/2461912.2462002

4. G. Bertasius J. Shi and L. Torresani. 2015 a. Deepedge: A multi-scale bifurcated deep network for top-down contour detection CVPR. 4380--4389. G. Bertasius J. Shi and L. Torresani. 2015 a. Deepedge: A multi-scale bifurcated deep network for top-down contour detection CVPR. 4380--4389.

5. G. Bertasius J. Shi and L. Torresani. 2015 b. High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision ICCV. 504--512. 10.1109/ICCV.2015.65 G. Bertasius J. Shi and L. Torresani. 2015 b. High-for-low and low-for-high: Efficient boundary detection from deep object features and its applications to high-level vision ICCV. 504--512. 10.1109/ICCV.2015.65

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