Thomas Brox Bored by Classification ConvNets? End-to-end Learning of other Computer Vision Tasks Thomas Brox University of Freiburg Germany Research funded by ERC Starting Grant VideoLearn, the German Research Foundation, and the Deutsche Telekom Stiftung
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Thomas Brox
Bored by Classification ConvNets?
End-to-end Learning of other Computer Vision Tasks
Thomas Brox
University of Freiburg
Germany
Research funded by ERC Starting Grant VideoLearn, the German Research Foundation, and the Deutsche Telekom Stiftung
Thomas Brox
Generative networks
U-Net: Multi-instance segmentation
FlowNet: Estimating optical flow
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Outline
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Typical ConvNet architecture
cat
Classification network
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Typical ConvNet architecture
cat
Classification network
cat
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Small grayoffice chair,side view
cat
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Up-convolutional network
Image generation
Related work: • Eigen et al. NIPS 2014: Network for depth map prediction• Long et al. CVPR 2015: Network for semantic segmentation
Alexey DosovitskiyCVPR 2015
New: Expanding network architecture
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Generating chair images with a network
Dosovitskiy et al., CVPR 2015
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Training set
Source: https://github.com/dimatura/seeing3d
3D chair datasetAubry et al. CVPR 2014
Rendering 809 chair styles From 62 viewpoints
Some of the rendered chairs
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Generating images of unseen views
Training set split into two subsets:
Source set: 62 viewpoints available (90% of all chair models)
Target set: fewer viewpoints available (10% of all models)
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Generating images of unseen views
8 azimuths available
4 azimuths available
2 azimuths available
1 azimuth available
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Comparison to baselines
Alexey DosovitskiyCVPR 2015
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Interpolation of chair styles
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Alexey DosovitskiyCVPR 2015
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Correspondences between chair instances
Alexey DosovitskiyCVPR 2015
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• Generate intermediate images with the network
• Track points with optical flow (LDOF) along the sequence
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Correspondences between chair instances
all easy difficult
Deformable Spatial Pyramid Matching (Kim et al. 2013)