Skip to main content

Advertisement

Springer Nature Link
Log in

Zero-shot leaning and hashing with binary visual similes

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Conventional zero-shot learning methods usually learn mapping functions to project image features into semantic embedding spaces, in which to find the nearest neighbors with predefined attributes. The predefined attributes including both seen classes and unseen classes are often annotated with high dimensional real values by experts, which costs a lot of human labors. In this paper, we propose a simple but effective method to reduce the annotation work. In our strategy, only unseen classes are needed to be annotated with several binary codes, which lead to only about one percent of original annotation work. In addition, we design a Visual Similes Annotation System (ViSAS) to annotate the unseen classes, and build both linear and deep mapping models and test them on four popular datasets, the experimental results show that our method can outperform the state-of-the-art methods in most circumstances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://uk.mathworks.com/help/matlab/ref/sylvester.html

References

  1. Akata Z, Reed S, Walter D, Lee H, Schiele B (2015) Evaluation of output embeddings for fine-grained image classification. In: CVPR, pp 2927–2936

  2. Akata Z, Malinowski M, Fritz M, Schiele B (2016) Multi-cue zero-shot learning with strong supervision. In: CVPR, pp 59–68

  3. Akata Z, Perronnin F, Harchaoui Z, Schmid C (2016) Label-embedding for image classification. IEEE TPAMI 38(7):1425–1438

    Article  Google Scholar 

  4. Al-Halah Z, Stiefelhagen R (2017) Automatic discovery, association estimation and learning of semantic attributes for a thousand categories. In: CVPR

  5. Bartels RH, Stewart GW (1972) Solution of the matrix equation AX + XB = C [F4]. Commun ACM 15(9):820–826

    Article  MATH  Google Scholar 

  6. Bucher M, Herbin S, Jurie F (2016) Improving semantic embedding consistency by metric learning for zero-shot classiffication. In: ECCV, pp 730–746. Springer

  7. Changpinyo S, Chao WL, Gong B, Sha F (2016) Synthesized classifiers for zero-shot learning. In: CVPR, pp 5327–5336

  8. Cheng Z, Shen J (2016) On very large scale test collection for landmark image search benchmarking. Signal Process 124:13–26

    Article  Google Scholar 

  9. Cheng Z, Ding Y, Zhu L, Kankanhalli M (2018) Aspect-aware latent factor model: Rating prediction with ratings and reviews. In: WWW

  10. Demirel B, Cinbis RG, Cinbis NI (2017) Attributes2classname: A discriminative model for attribute-based unsupervised zero-shot learning. In: CVPR

  11. Ding Z, Shao M, Fu Y (2017) Low-rank embedded ensemble semantic dictionary for zero-shot learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2050–2058

  12. Farhadi A, Endres I, Hoiem D, Forsyth D (2009) Describing objects by their attributes. In: CVPR, pp 1778–1785. IEEE

  13. Ferrari V, Zisserman A (2008) Learning visual attributes. In: NIPS, pp 433–440

  14. Frome A, Corrado GS, Shlens J, Bengio S, Dean J, Mikolov T, et al. (2013) Devise: A deep visual-semantic embedding model. In: NIPS, pp 2121–2129

  15. Fu Y, Sigal L (2016) Semi-supervised vocabulary-informed learning. In: CVPR, pp 5337–5346

  16. Fu Y, Hospedales TM, Xiang T, Fu Z, Gong S (2014) Transductive multi-view embedding for zero-shot recognition and annotation. In: ECCV, pp 584–599. Springer

  17. Fu Z, Xiang T, Kodirov E, Gong S (2015) Zero-shot object recognition by semantic manifold distance. In: CVPR, pp 2635–2644

  18. Guo Y, Ding G, Jin X, Wang J (2016) Transductive zero-shot recognition via shared model space learning. In: AAAI, pp 3494–5000

  19. Guo Y, Ding G, Han J, Gao Y (2017) Sitnet: Discrete similarity transfer network for zero-shot hashing. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 1767–1773

  20. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, pp 770–778

  21. Huang S, Elhoseiny M, Elgammal A, Yang D (2015) Learning hypergraph-regularized attribute predictors. In: CVPR, pp 409–417

  22. Kodirov E, Xiang T, Fu Z, Gong S (2015) Unsupervised domain adaptation for zero-shot learning. In: ICCV, pp 2452–2460

  23. Kodirov E, Xiang T, Gong S (2017) Semantic autoencoder for zero-shot learning. In: CVPR

  24. Krizhevsky A, Hinton G (2009) Learning multiple layers of features from tiny images

  25. Lampert CH, Nickisch H, Harmeling S (2014) Attribute-based classification for zero-shot visual object categorization. IEEE TPAMI 36(3):453–465

    Article  Google Scholar 

  26. Li J, Zhao J, Lu K (2016) Joint feature selection and structure preservation for domain adaptation. In: IJCAI, pp 1697–1703

  27. Li J, Wu Y, Zhao J, Lu K (2017) Low-rank discriminant embedding for multiview learning. IEEE Trans Cybern 47(11):3516–3529

    Article  Google Scholar 

  28. Li J, Lu K, Huang Z, Zhu L, Shen HT (2018) Transfer independently together: A generalized framework for domain adaptation. IEEE Transactions on Cybernetics

  29. Liu Y, Gao Q, Li J, Han J, Shao L (2018) Zero shot learning via low-rank embedded semantic autoencoder. In: IJCAI, pp 2490–2496

  30. Long Y, Shao L (2017) Describing unseen classes by exemplars: Zero-shot learning using grouped simile ensemble. In: WACV, pp 907–915. IEEE

  31. Long Y, Liu L, Shao L, Shen F, Ding G, Han J (2017) From zero-shot learning to conventional supervised classification: Unseen visual data synthesis. In: CVPR

  32. Lu J, Li J, Yan Z, Zhang C (2017) Zero-shot learning by generating pseudo feature representations. In: CVPR

  33. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: ICLR

  34. Nie L, Yan S, Wang M, Hong R, Chua TS (2012) Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM international conference on Multimedia, pp 59–68. ACM

  35. Nie L, Zhao YL, Wang X, Shen J, Chua TS (2014) Learning to recommend descriptive tags for questions in social forums. ACM Trans Inf Syst (TOIS) 32(1):5

    Article  Google Scholar 

  36. Norouzi M, Mikolov T, Bengio S, Singer Y, Shlens J, Frome A, Corrado GS, Dean J (2014) Zero-shot learning by convex combination of semantic embeddings. In: ICLR

  37. Patterson G, Xu C, Su H, Hays J (2014) The sun attribute database: Beyond categories for deeper scene understanding. IJCV 108(1-2):59–81

    Article  Google Scholar 

  38. Qiao R, Liu L, Shen C, van den Hengel A (2016) Less is more: zero-shot learning from online textual documents with noise suppression. In: CVPR, pp 2249–2257

  39. Romera-Paredes B, Torr P (2015) An embarrassingly simple approach to zero-shot learning. In: ICML, pp 2152–2161

  40. Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 37–45

  41. Socher R, Ganjoo M, Manning CD, Ng A (2013) Zero-shot learning through cross-modal transfer. In: NIPS, pp 935–943

  42. Welinder P, Branson S, Mita T, Wah C, Schroff F, Belongie S, Perona P (2010) Caltech-ucsd birds, pp 200

  43. Xian Y, Akata Z, Sharma G, Nguyen Q, Hein M, Schiele B (2016) Latent embeddings for zero-shot classification. In: CVPR, pp 69–77

  44. Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. In: CVPR

  45. Yang Y, Luo Y, Chen W, Shen F, Shao J, Shen H T (2016) Zero-shot hashing via transferring supervised knowledge. In: Proceedings of the 2016 ACM on multimedia conference, pp 1286–1295. ACM

  46. Yang Y, Luo Y, Chen W, Shen F, Shao J, Shen H T (2016) Zero-shot hashing via transferring supervised knowledge. In: ACM MM, pp 1286–1295. ACM

  47. Zhang Z, Saligrama V (2015) Zero-shot learning via semantic similarity embedding. In: ICCV, pp 4166–4174

  48. Zhang L, Li X, Nie L, Yan Y, Zimmermann R (2016) Semantic photo retargeting under noisy image labels. ACM Trans Multimed Comput Commun Appl (TOMM) 12(3):37

    Google Scholar 

  49. Zhang L, Xiang T, Gong S (2017) Learning a deep embedding model for zero-shot learning. In: CVPR

  50. Zhang H, Liu L, Long Y, Shao L (2018) Unsupervised deep hashing with pseudo labels for scalable image retrieval. IEEE Trans Image Process 27(4):1626–1638

    Article  MathSciNet  Google Scholar 

  51. Zhang H, Long Y, Yang W, Shao L (2019) Dual-verification network for zero-shot learning. Inform Sci 470:43–57

    Article  MathSciNet  Google Scholar 

  52. Zhu L, Shen J, Liu X, Xie L, Nie L (2016) Learning compactvisual representation with canonical views for robust mobile landmark search. In: IJCAI

  53. Zhu L, Shen J, Xie L (2016) Topic hypergraph hashing for mobile imageretrieval. In: ACM MM

  54. Zhu L, Huang Z, Chang X, Song J, Shen H T (2017) Exploring consistent preferences:discrete hashing with pair-exemplar for scalable landmark search. In: ACM MM

  55. Zhu L, Huang Z, Liu X, Xie L (2017) Discrete multi-modal hashing with canonical views for robust mobile landmark search. IEEE TMM 19(9):2066–2079

    Google Scholar 

  56. Zhu L, Huang Z, Liu X, Xie L (2017) Unsupervised topic hypergraph hashing for efficient mobile image retrieval. IEEE Trans Cybern 19(9):2066–2079

    Google Scholar 

  57. Zhu L, Shen J, Xie L, Cheng Z (2017) Unsupervised visual hashing with semantic assistance for efficient content-based web image retrieval. IEEE TKDE 29 (2):472–486

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Haofeng Zhang

  2. Open Laboratory, School of Computing, Newcastle University, Newcastle upon Tyne, UK

    Yang Long

  3. Inception Institute of Artificial Intelligence (IIAI), Abu Dhabi, United Arab Emirates

    Ling Shao

Authors
  1. Haofeng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yang Long
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ling Shao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Haofeng Zhang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by National Natural Science Foundation of China (No.61872187) and the Major Special Project of Core Electronic Devices, High-end Generic Chips and Basic Software (No.2015ZX01041101).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Long, Y. & Shao, L. Zero-shot leaning and hashing with binary visual similes. Multimed Tools Appl 78, 24147–24165 (2019). https://doi.org/10.1007/s11042-018-6842-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6842-3

Keywords