Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

Seunghoon Hong; Junhyuk Oh; Honglak Lee; Bohyung Han

doi:10.1109/cvpr.2016.349

proceedings article Jun 01, 2016

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

Seunghoon Hong Junhyuk Oh Honglak Lee Bohyung Han

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 3204-3212 · IEEE

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References

38

[1]

Conditional Random Fields as Recurrent Neural Networks

Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes et al.

2015 IEEE International Conference on Computer Vis... 10.1109/iccv.2015.179

[2]

saenko "Adapting visual category models to new domains" ECCV (2010)

[3]

LabelMe: A Database and Web-Based Tool for Image Annotation

Bryan C. Russell, Antonio Torralba, Kevin P. Murphy et al.

International Journal of Computer Vision 2010 10.1007/s11263-007-0090-8

[4]

From image-level to pixel-level labeling with Convolutional Networks

Pedro O. Pinheiro, Ronan Collobert

2015 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2015.7298780

[5]

10.1109/cvpr.2014.187

[6]

xu "Show, attend and tell: Neural image caption generation with visual attention" ICML (2015)

[7]

10.1109/tpami.2013.197

[8]

tang "Learning generative models with visual attention" NIPS (2014)

[9]

simonyan "Very deep convolutional networks for large-scale image recognition" ICLRE (2015)

[10]

hariharan "Simultaneous detection and segmentation" ECCV (2014)

[11]

hariharan "Hyper-columns for object segmentation and fine-grained localization" CVPR (2015)

[12]

hoffman "LSDA: large scale detection through adaptation" NIPS (2014)

[13]

hong "Decoupled deep neural network for semi-supervised semantic segmentation" NIPS (2015)

[14]

Caffe

Yangqing Jia, Evan Shelhamer, Jeff Donahue et al.

Proceedings of the 22nd ACM international conferen... 10.1145/2647868.2654889

[15]

khosla "Undoing the damage of dataset bias" ECCV (2012)

[16]

kingma "Adam: A method for stochastic optimization" ICLRE (2015)

[17]

krähenbühl "Efficient inference in fully connected crfs with gaussian edge potentials" NIPS (2011)

[18]

larochelle "Learning to combine foveal glimpses with a third-order boltzmann machine" NIPS (2010)

[19]

lim "Transfer learning by borrowing examples for multiclass object detection" NIPS (2011)

[20]

Constrained Convolutional Neural Networks for Weakly Supervised Segmentation

Deepak Pathak, Philipp Krähenbühl, Trevor Darrell

2015 IEEE International Conference on Computer Vis... 10.1109/iccv.2015.209

[21]

chen "Semantic image segmentation with deep convolutional nets and fully connected CRFs" ICLRE (2015)

[22]

papandreou "Weakly- and semi-supervised learning of a DCNN for semantic image segmentation" ICCV (2015)

[23]

bazzani "Learning attentional policies for object tracking and recognition in video with deep networks" ICML (2011)

[24]

deng "ImageNet: a large-scale hierarchical image database" CVPR (2009)

[25]

pathak "Fully convolutional multi-class multiple instance learning" ICLRE (2015)

[26]

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Jifeng Dai, Kaiming He, Jian Sun

2015 IEEE International Conference on Computer Vis... 10.1109/iccv.2015.191

[27]

ganin "Unsupervised domain adaptation by backpropagation" ICML (2015)

[28]

The Pascal Visual Object Classes (VOC) Challenge

Mark Everingham, Luc Van Gool, Christopher K. I. Williams et al.

International Journal of Computer Vision 10.1007/s11263-009-0275-4

[29]

ba "Multiple object recognition with visual attention" ICLRE (2015)

[30]

10.1109/iccv.2011.6126344

[31]

alexe "Searching for objects driven by context" NIPS (2012)

[32]

lin "Microsoft COCO: common objects in context" ECCV (2014)

[33]

A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

D. Martin, C. Fowlkes, D. Tal et al.

Proceedings Eighth IEEE International Conference o... 10.1109/iccv.2001.937655

[34]

Fully convolutional networks for semantic segmentation

Jonathan Long, Evan Shelhamer, Trevor Darrell

2015 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2015.7298965

[35]

mnih "Recurrent models of visual attention" NIPS (2014)

[36]

10.1109/tpami.2013.53

[37]

Learning Deconvolution Network for Semantic Segmentation

Hyeonwoo Noh, Seunghoon Hong, Bohyung Han

2015 IEEE International Conference on Computer Vis... 10.1109/iccv.2015.178

[38]

Feedforward semantic segmentation with zoom-out features

Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich

2015 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2015.7298959

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References

Details

Published: Jun 01, 2016
Pages: 3204-3212

Authors

Stanford University, Stanford, CA

B

Bohyung Han

Cite This Article

Seunghoon Hong, Junhyuk Oh, Honglak Lee, et al. (2016). Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3204-3212. https://doi.org/10.1109/cvpr.2016.349

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

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