Advertisement

Adaptive Margin Diversity Regularizer for Handling Data Imbalance in Zero-Shot SBIR

Conference paper
  • 732 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12350)

Abstract

Data from new categories are continuously being discovered, which has sparked significant amount of research in developing approaches which generalize to previously unseen categories, i.e. zero-shot setting. Zero-shot sketch-based image retrieval (ZS-SBIR) is one such problem in the context of cross-domain retrieval, which has received lot of attention due to its various real-life applications. Since most real-world training data have a fair amount of imbalance; in this work, for the first time in literature, we extensively study the effect of training data imbalance on the generalization to unseen categories, with ZS-SBIR as the application area. We evaluate several state-of-the-art data imbalance mitigating techniques and analyze their results. Furthermore, we propose a novel framework AMDReg (Adaptive Margin Diversity Regularizer), which ensures that the embeddings of the sketches and images in the latent space are not only semantically meaningful, but they are also separated according to their class-representations in the training set. The proposed approach is model-independent, and it can be incorporated seamlessly with several state-of-the-art ZS-SBIR methods to improve their performance under imbalanced condition. Extensive experiments and analysis justify the effectiveness of the proposed AMDReg for mitigating the effect of data imbalance for generalization to unseen classes in ZS-SBIR.

Notes

Acknowledgement

This work is partly supported through a research grant from SERB, Department of Science and Technology, Government of India.

References

  1. 1.
    Barandela, R., Rangel, E., Sanchez, J.S., Ferri, F.J.: Restricted decontamination for the imbalanced training sample problem. Springer, Iberoamerican Congress on Pattern Recognition (2003)CrossRefGoogle Scholar
  2. 2.
    Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS (2019)Google Scholar
  3. 3.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artifi. Intell. Res. 16, 321–357 (2002)CrossRefGoogle Scholar
  4. 4.
    Cui, Y., Jia, M., Lin, T.Y., Song, Y.: Class-balanced loss based on effective number of samples. In: CVPR (2019)Google Scholar
  5. 5.
    Dey, S., Riba, P., Dutta, A., Llados, J., Song, Y.Z.: Doodle to search: practical zero-shot sketch-based image retrieval. In: CVPR (2019)Google Scholar
  6. 6.
    Dutta, A., Akata, Z.: Sematically tied paired cycle consistency for zero-shot sketch-based image retrieval. In: CVPR (2019)Google Scholar
  7. 7.
    Dutta, T., Biswas, S.: Style-guided zero-shot sketch-based image retrieval. In: BMVC (2019)Google Scholar
  8. 8.
    Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? ACM TOG 31(4), 1–10 (2012)Google Scholar
  9. 9.
    Felix, R., Kumar, V.B., Reid, I., Carneiro, G.: Multi-modal cycle-consistent generalized zero-shot learning. In: ECCV (2018)Google Scholar
  10. 10.
    Frome, A., et al.: Devise: A deep visual-semantic embedding model. In: NeurIPS (2013)Google Scholar
  11. 11.
    Hayat, M., Khan, S., Zamir, S.W., Shen, J., Shao, L.: Gaussian affinity for max-margin class imbalanced learning. In: ICCV (2019)Google Scholar
  12. 12.
    Hu, R., Collomosse, J.: A performance evaluation of gradient field hog descriptor for sketch based image retrieval. CVIU 117(7), 790–806 (2013)Google Scholar
  13. 13.
    Kodirov, E., Xiang, T., Gong, S.: Semantic autoencoder for zero-shot learning. In: CVPR (2017)Google Scholar
  14. 14.
    Lin, T.Y., Goyal, P., Girshiick, R., He, K., Dollar, P.: Focal loss for dense object detection. arXiv:1708.02002 [cs.CV] (2018)
  15. 15.
    Liu, L., Shen, F., Shen, Y., Liu, X., Shao, L.: Deep sketch hashing: fast free-hand sketch-based image retrieval. In: CVPR (2017)Google Scholar
  16. 16.
    Liu, Q., Xie, L., Wang, H., Yuille, A.: Semantic-aware knowledge preservation for zero-shot sketch-based image retrieval. In: ICCV (2019)Google Scholar
  17. 17.
    Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu, S.X.: Large-scale long-tailed recognition in an open world. In: CVPR (2019)Google Scholar
  18. 18.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. NeurIPS (2013)Google Scholar
  19. 19.
    Mishra, A., Reddy, S.K., Mittal, A., Murthy, H.A.: A generative model for zero-shot learning using conditional variational auto-encoders. CVPR-W (2018)Google Scholar
  20. 20.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)Google Scholar
  21. 21.
    Qi, H., Brown, M., Lowe, D.G.: Low-shot learning with imprinted weights. In: CVPR (2018)Google Scholar
  22. 22.
    Qi, Y., Song, Y.Z., Zhang, H., Liu, J.: Sketch-based image retrieval via siamese convolutional neural network. In: ICIP (2016)Google Scholar
  23. 23.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Li, F.F.: Imagenet: large-scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Saavedra, J.M., Barrios, J.M.: Sketch-based image retrieval using learned keyshapes (lks). In: BMVC (2015)Google Scholar
  25. 25.
    Sangkloy, P., Burnell, N., Ham, C., Hays, J.: The sketchy database: learning to retrieve badly drawn bunnies. ACM TOG 35(4), 1–12 (2016)CrossRefGoogle Scholar
  26. 26.
    Shen, Y., Liu, L., Shen, F., Shao, L.: Zero-shot sketch-image hashing. In: CVPR (2018)Google Scholar
  27. 27.
    Socher, R., Ganjoo, M., Manning, C.D., Ng, A.: Zero-shot learning through cross-modal transfer. In: NeurIPS (2013)Google Scholar
  28. 28.
    Wang, M., Wang, C., Wu, J.X., Zhang, J.: Community detection in social networks: an in-depth benchmarking study with a procedure-oriented framework. In: VLDB (2015)Google Scholar
  29. 29.
    Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: CVPR (2018)Google Scholar
  30. 30.
    Xian, Y., Sharma, S., Schiele, B., Akata, Z.: f-vaegan-d2: A feature generating framework for any-shot learning. In: CVPR (2019)Google Scholar
  31. 31.
    Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861 (2016)
  32. 32.
    Yelamarthi, S.K., Reddy, S.K., Mishra, A., Mittal, A.: A zero-shot framework for sketch-based image retrieval. In: ECCV (2018)Google Scholar
  33. 33.
    Yu, Q., Yang, Y., Liu, F., Song, Y.Z., Xiang, T., Hospedales, T.M.: Sketch-a-net that beats humans. In: BMVC (2015)Google Scholar
  34. 34.
    Zhang, J., Liu, S., Zhang, C., Ren, W., Wang, R., Cao, X.: Sketchnet: sketch classification with web images. In: CVPR (2016)Google Scholar
  35. 35.
    Zhang, J., et al.: Generative domain-migration hashing for sketch-to-image retrieval. In: ECCV (2018)Google Scholar
  36. 36.
    Zhang, R., Lin, L., Zhang, R., Zuo, W., Zhang, L.: Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Trans. Image Process. 24(12), 4766–4779 (2015)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Zhang, Z., Saligrama, V.: Zero-shot learning via joint latent similarity embedding. In: CVPR (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Indian Institute of ScienceBangaloreIndia

Personalised recommendations