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Attention Awareness Multiple Instance Neural Network

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Artificial Neural Networks and Machine Learning – ICANN 2022 (ICANN 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13531))

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Abstract

Multiple instance learning is qualified for many pattern recognition tasks with weakly annotated data. The combination of artificial neural network and multiple instance learning offers an end-to-end solution and has been widely utilized. However, challenges remain in two-folds. Firstly, current MIL pooling operators are usually pre-defined and lack flexibility to mine key instances. Secondly, in current solutions, the bag-level representation can be inaccurate or inaccessible. To this end, we propose an attention awareness multiple instance neural network framework in this paper. It consists of an instance-level classifier, a trainable MIL pooling operator based on spatial attention and a bag-level classification layer. Exhaustive experiments on a series of pattern recognition tasks demonstrate that our framework outperforms many state-of-the-art MIL methods and validates the effectiveness of our proposed attention MIL pooling operators.

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References

  1. Amores, J.: Multiple instance classification: review, taxonomy and comparative study. Artif. Intell. 201(4), 81–105 (2013)

    Article  MathSciNet  Google Scholar 

  2. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Neural Information Processing Systems (2003)

    Google Scholar 

  3. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2009)

    Google Scholar 

  4. Bi, Q., Qin, K., Li, Z.L., Zhang, H., Xu, K.: Multiple instance dense connected convolution neural network for aerial image scene classification. In: IEEE International Conference on Image Processing (2019)

    Google Scholar 

  5. Bi, Q., Qin, K., Zhang, H., Xia, G.S.: Local semantic enhanced convnet for aerial scene recognition. IEEE Trans. Image Process. 30, 6498–6511 (2021)

    Article  Google Scholar 

  6. Bi, Q., Zhou, B., Qin, K., Ye, Q., Xia, G.S.: All grains, one scheme (agos): Learning multi-grain instance representation for aerial scene classification. arXiv preprint arXiv:2205.03371 (2022)

  7. Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)

    Article  Google Scholar 

  8. Hou, L., Samaras, D., Kurc, T.M., Gao, Y., Davis, J.E., Saltz, J.H.: Patch-based convolutional neural network for whole slide tissue image classification. In: IEEE Conference on Computer Vision & Pattern Recognition (2016)

    Google Scholar 

  9. Ilse, M., Tomczak, J.M., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning (2018)

    Google Scholar 

  10. Kumar, R., Melih, K.: Variational weakly-supervised gaussian processes. In: 27th British Machine Vision Conference (2019)

    Google Scholar 

  11. Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: Fifteenth International Conference on Machine Learning (1998)

    Google Scholar 

  12. Peng, T., Wang, X., Xiang, B., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  13. Sirinukunwattana, K., Raza, S., Tsang, Y.W., Snead, D., Cree, I., Rajpoot, N.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)

    Article  Google Scholar 

  14. Vatsavai, R.R.: Gaussian multiple instance learning approach for mapping the slums of the world using very high resolution imagery. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2013)

    Google Scholar 

  15. Wang, Q., Yuan, Y., Yan, P., Li, X.: Saliency detection by multiple-instance learning. IEEE Trans. Cybern. 43(2), 660–672 (2013)

    Article  Google Scholar 

  16. Wang, Q., Si, L., Zhang, D.: A discriminative data-dependent mixture-model approach for multiple instance learning in image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 660–673. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_47

    Chapter  Google Scholar 

  17. Wang, X., Yan, Y., Peng, T., Xiang, B., Liu, W.: Revisiting multiple instance neural networks. Pattern Recogn. 74, 15–24 (2016)

    Article  Google Scholar 

  18. Xia, G., et al.: Aid: A benchmark dataset for performance evaluation of aerial scene classification. IEEE Trans. Geosci. & Remote Sensing 55(7), 3965–3981 (2017)

    Google Scholar 

  19. Xu, Y.Y.: Multiple-instance learning based decision neural networks for image retrieval and classification. Neurocomputing 171(C), 826–836 (2015)

    Google Scholar 

  20. Yan, S., Zhu, X., Liu, G., Wu, J.: Sparse multiple instance learning as document classification. Multimedia Tools Appl. 76(3), 4553–4570 (2016). https://doi.org/10.1007/s11042-016-3567-z

    Article  Google Scholar 

  21. Yi, Y., Newsam, S.: Geographic image retrieval using local invariant features. IEEE Trans. Geosci. Remote Sens. 51(2), 818–832 (2013)

    Article  Google Scholar 

  22. Yun, W., Li, J., Metze, F.: Comparing the max and noisy-or pooling functions in multiple instance learning for weakly supervised sequence learning tasks (2018). arXiv preprint arXiv:1805.10201

  23. Zaheer, M., Kottur, S., Ravanbhakhsh, S., Poczos, B., Smola, A.: Deep sets. In: Conference and Workshop on Neural Information Processing Systems (2017)

    Google Scholar 

  24. Zhang, M.L., Zhou, Z.H.: Improve multi-instance neural networks through feature selection. Neural Process. Lett. 19(1), 1–10 (2004)

    Article  Google Scholar 

  25. Zhang, M.L., Zhou, Z.H.: Adapting RBF neural networks to multi-instance learning. Neural Process. Lett. 23(1), 1–26 (2006)

    Article  MathSciNet  Google Scholar 

  26. Zhou, B., Yi, J., Bi, Q.: Differential convolution feature guided deep multi-scale multiple instance learning for aerial scene classification. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4595–4599. IEEE (2021)

    Google Scholar 

  27. Zhou, Z.H., Sun, Y.Y., Li, Y.F.: Multi-instance learning by treating instances as non-IID samples. In: International Conference on Machine Learning. pp. 1249–1256 (2009)

    Google Scholar 

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Authors and Affiliations

  1. Wuhan University, Wuhan, 430079, Hubei, China

    Jingjun Yi & Beichen Zhou

Authors
  1. Jingjun Yi
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  2. Beichen Zhou
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Corresponding author

Correspondence to Jingjun Yi .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teeside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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Yi, J., Zhou, B. (2022). Attention Awareness Multiple Instance Neural Network. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13531. Springer, Cham. https://doi.org/10.1007/978-3-031-15934-3_48

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  • DOI: https://doi.org/10.1007/978-3-031-15934-3_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15933-6

  • Online ISBN: 978-3-031-15934-3

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