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In this work we propose a technique that transfers supervision between images from different modalities.
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Multimodal deep learning
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Zero-shot learning through cross-modal transfer
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
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Unsupervised Domain Adaptation by Backpropagation
Y. Ganin and V. Lempitsky · 2014
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Domain adaptive neural networks for object recognition
M. Ghifary, W. B. Kleijn, and M. Zhang · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Learning rich features from RGB-D images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik · 2014
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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Multimodal learning with deep boltzmann machines
N. Srivastava and R. Salakhutdinov · 2014
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Deep domain confusion: Maximizing for domain invariance
E. Tzeng, J. Hoffman, N. Zhang, K. Saenko, and T. Darrell · 2014
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Fast R-CNN
R. Girshick · 2015
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Finding action tubes
G. Gkioxari and J. Malik · 2015
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Aligning 3D models to RGB-D images of cluttered scenes
S. Gupta, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Beyond Bounding Boxes: Precise Localization of Objects in Images
B. Hariharan · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Understanding image representations by measuring their equivariance and equivalence
K. Lenc and A. Vedaldi · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning transferable features with deep adaptation networks
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Sun rgb-d: A rgb-d scene understanding benchmark suite
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Viewpoints and keypoints
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