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Learning with side information through modality hallucination (2016)

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Learning with side information through modality hallucination, J. Hoffman et al., CVPR2016

http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Hoffman_Learning_With_Side_CVPR_2016_paper.html

Published in: Engineering
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Learning with side information through modality hallucination (2016)

  1. 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um LEARNING WITH SIDE INFOR- MATION THROUGH MODALITY HALLUCINATION (2016) 1
  2. 2. Terry Taewoong Um (terry.t.um@gmail.com) BEYOND SUPERVISED / UNSUPERVISED 2 supervised learning semi-supervised learning weakly-supervised learning “Is object localization for free? Weakly-supervised learning with convolutional neural networks (2015)”, M. Oquab et al. “Bayesian Semisupervised Learning with Deep Generative Models (2017)”, J. Gordon et al. • Various learning scenarios • Learning with side information (modality) (training) (test)
  3. 3. Terry Taewoong Um (terry.t.um@gmail.com) MISSING INPUT DURING TEST 3 (training) (test) Couch zero- padding…? ???
  4. 4. Terry Taewoong Um (terry.t.um@gmail.com) MISSING INPUT DURING TEST 4 (training) (test) Couch ??? generate
  5. 5. Terry Taewoong Um (terry.t.um@gmail.com) MISSING INPUT DURING TEST 5 (training) ??? (test) generate Couch
  6. 6. Terry Taewoong Um (terry.t.um@gmail.com) HALLUCINATION 6 (training) (test) The red & blue should make similar features :
  7. 7. Terry Taewoong Um (terry.t.um@gmail.com) RELATED WORKS 7 • RGB-D detection : exploit depth images • Transfer learning and domain adaptation : transfer the knowledge from a depth image to a RGB image • Learning using privileged information : Training with a teacher x : X-ray x* : Clinician’s interpretation y : Cancer Y/N • Distillation : the output from one network is used as the target for a new network.
  8. 8. LOSS FUNCTION 8 Hallucination Classification Localization
  9. 9. LOSS FUNCTION 9 Hallucination Classification Localization
  10. 10. LOSS FUNCTION 10 Hallucination Classification Localization
  11. 11. SEVERAL ISSUES 11 Terry Taewoong Um (terry.t.um@gmail.com) • Training & Initialization : First train the RGB & D-Net, and copy the D-Net to H-Net • Which layer to hallucinate? Pool5
  12. 12. RESULTS 12 Terry Taewoong Um (terry.t.um@gmail.com) • With new dataset (Pascal voc 2007) • With trained dataset (NYUD2)
  13. 13. RESULTS 13 Terry Taewoong Um (terry.t.um@gmail.com) RGB-D-H (O) RGB (X) RGB-D-H (X) RGB (O)
  14. 14. SUMMARY 14 Terry Taewoong Um (terry.t.um@gmail.com) • If you have a missing modality at test time, (Or if you have additional modality at training time,) hallucinate! • Good idea, but not a in-depth understanding… • How can a RGB image “imagine” its missing depth image? (Can we visualize • Is the learned H-net generalizable to new images? • Is this method effective to other modalities as well? • Can we propose a domain-specific hallucination architecture? • We may exploit more information (modalities) at training time than run-time • Beyond supervised / unsupervised settings….

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