Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
CNN-PS: CNN-Based Photometric Stereo for General Non-convex Surfaces Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Dynamic Conditional Networks for Few-Shot Learning

Authors : Fang Zhao, Jian Zhao, Shuicheng Yan, Jiashi Feng

Published in: Computer Vision – ECCV 2018

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper proposes a novel Dynamic Conditional Convolutional Network (DCCN) to handle conditional few-shot learning, i.e, only a few training samples are available for each condition. DCCN consists of dual subnets: DyConvNet contains a dynamic convolutional layer with a bank of basis filters; CondiNet predicts a set of adaptive weights from conditional inputs to linearly combine the basis filters. In this manner, a specific convolutional kernel can be dynamically obtained for each conditional input. The filter bank is shared between all conditions thus only a low-dimension weight vector needs to be learned. This significantly facilitates the parameter learning across different conditions when training data are limited. We evaluate DCCN on four tasks which can be formulated as conditional model learning, including specific object counting, multi-modal image classification, phrase grounding and identity based face generation. Extensive experiments demonstrate the superiority of the proposed model in the conditional few-shot learning setting.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter CNN-PS: CNN-Based Photometric Stereo for General Non-convex Surfaces
next chapter Deep Factorised Inverse-Sketching
Literature
1.
Amodei, D., et al.: Deep speech 2: end-to-end speech recognition in English and mandarin. In: International Conference on Machine Learning, pp. 173–182 (2016)
2.
Berthelot, D., Schumm, T., Metz, L.: Began: boundary equilibrium generative adversarial networks. arXiv preprint arXiv:​1703.​10717 (2017)
3.
Bertinetto, L., Henriques, J.F., Valmadre, J., Torr, P., Vedaldi, A.: Learning feed-forward one-shot learners. In: Advances in Neural Information Processing Systems, pp. 523–531 (2016)
4.
Bloom, P.: How Children Learn the Meanings of Words. The MIT Press (2000)
5.
Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a “siamese” time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744 (1994)
6.
Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (chapelle, o. et al., eds. 2006)[book reviews]. IEEE Trans. Neural Netw. 20(3), 542–542 (2009)
7.
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. arXiv preprint arXiv:​1405.​3531 (2014)
8.
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 766–774 (2014)
9.
10.
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
11.
Guadarrama, S., et al.: Long short-term memory. Neural Comput. (1997)
12.
13.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
14.
Hertz, T., Hillel, A.B., Weinshall, D.: Learning a kernel function for classification with small training samples. In: Proceedings of the International Conference on Machine Learning, pp. 401–408. ACM (2006)
15.
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: Densely connected convolutional networks. arXiv preprint arXiv:​1608.​06993 (2016)
16.
Huiskes, M.J., Lew, M.S.: The MIR flickr retrieval evaluation. In: Proceedings of the ACM International Conference on Multimedia Information Retrieval (2008)
17.
Krause, E.A., Zillich, M., Williams, T.E., Scheutz, M.: Learning to recognize novel objects in one shot through human-robot interactions in natural language dialogues (2014)
18.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
19.
Kumar, A., et al.: Ask me anything: dynamic memory networks for natural language processing. In: International Conference on Machine Learning, pp. 1378–1387 (2016)
20.
Lake, B.M., Salakhutdinov, R.R., Tenenbaum, J.: One-shot learning by inverting a compositional causal process. In: Advances in Neural Information Processing Systems, pp. 2526–2534 (2013)
21.
Lim, J.J., Salakhutdinov, R.R., Torralba, A.: Transfer learning by borrowing examples for multiclass object detection. In: Advances in Neural Information Processing Systems, pp. 118–126 (2011)
22.
Lin, T.Y., RoyChowdhury, A., Maji, S.: Bilinear CNN models for fine-grained visual recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1457 (2015)
23.
Mehrotra, A., Dukkipati, A.: Generative adversarial residual pairwise networks for one shot learning. arXiv preprint arXiv:​1703.​08033 (2017)
24.
25.
Movshovitz-Attias, Y., Yu, Q., Stumpe, M.C., Shet, V., Arnoud, S., Yatziv, L.: Ontological supervision for fine grained classification of street view storefronts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1693–1702 (2015)
26.
27.
Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition
28.
Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k entities: collecting region-to-phrase correspondences for richer imageto-sentence models. Int. J. Comput. Vis. 123(1), 74–93 (2017)MathSciNetCrossRef
29.
Plummer1, B.A., Mallya, A., Cervantes, C.M., Hockenmaier, J., Lazebnik., S.: Phrase localization and visual relationship detection with comprehensive image-language cues. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
30.
Plummer1, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C.: Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
31.
Ravi, S., Larochelle, H.: Optimization as a model for few-shot learning (2016)
32.
33.
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. In: Advances in Neural Information Processing Systems, pp. 3630–3638 (2016)
34.
Wang, L., Li, Y., Lazebnik, S.: Earning deep structure preserving image-text embeddings. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
35.
36.
37.
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:​1609.​08144 (2016)
38.
Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Comput. Linguist. (2014)
39.
Zhang, Z., Song, Y., Qi, H.: Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
40.
Zhao, J., Xiong, L., Jayashree, K., et al.: Dual-agent GANs for photorealistic and identity preserving profile face synthesis. In: Advances in Neural Information Processing Systems, pp. 66–76 (2017)
41.
Zhu, X., Vondrick, C., Fowlkes, C.C., Ramanan, D.: Do we need more training data? Int. J. Comput. Vis. 119(1), 76–92 (2016)MathSciNetCrossRef
42.
Zhu, X.: Semi-supervised learning literature survey (2005)
43.
Metadata
Title
Dynamic Conditional Networks for Few-Shot Learning
Authors
Fang Zhao
Jian Zhao
Shuicheng Yan
Jiashi Feng
Copyright Year
2018
Book
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01266-3

Electronic ISBN: 978-3-030-01267-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01267-0_2

Premium Partner

    Image Credits