Contextual Diversity for Active Learning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12361)


Requirement of large annotated datasets restrict the use of deep convolutional neural networks (CNNs) for many practical applications. The problem can be mitigated by using active learning (AL) techniques which, under a given annotation budget, allow to select a subset of data that yields maximum accuracy upon fine tuning. State of the art AL approaches typically rely on measures of visual diversity or prediction uncertainty, which are unable to effectively capture the variations in spatial context. On the other hand, modern CNN architectures make heavy use of spatial context for achieving highly accurate predictions. Since the context is difficult to evaluate in the absence of ground-truth labels, we introduce the notion of contextual diversity that captures the confusion associated with spatially co-occurring classes. Contextual Diversity (CD) hinges on a crucial observation that the probability vector predicted by a CNN for a region of interest typically contains information from a larger receptive field. Exploiting this observation, we use the proposed CD measure within two AL frameworks: (1) a core-set based strategy and (2) a reinforcement learning based policy, for active frame selection. Our extensive empirical evaluation establish state of the art results for active learning on benchmark datasets of Semantic Segmentation, Object Detection and Image classification. Our ablation studies show clear advantages of using contextual diversity for active learning. The source code and additional results are available at



The authors acknowledge the partial support received from the Infosys Center for Artificial Intelligence at IIIT-Delhi. This work has also been partly supported by the funding received from DST through the IMPRINT program (IMP/2019/000250).

Supplementary material

504471_1_En_9_MOESM1_ESM.pdf (4.7 mb)
Supplementary material 1 (pdf 4783 KB)


  1. 1.
    Arazo, E., Ortego, D., Albert, P., O’Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning (2019)Google Scholar
  2. 2.
    Beluch, W.H., Genewein, T., Nürnberger, A., Köhler, J.M.: The power of ensembles for active learning in image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9368–9377 (2018)Google Scholar
  3. 3.
    Bilgic, M., Getoor, L.: Link-based active learning. In: NIPS Workshop on Analyzing Networks and Learning with Graphs (2009)Google Scholar
  4. 4.
    Caron, M., Bojanowski, P., Joulin, A., Douze, M.: Deep clustering for unsupervised learning of visual features. In: European Conference on Computer Vision (2018)Google Scholar
  5. 5.
    Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3213–3223 (2016)Google Scholar
  6. 6.
    Dabak, A.G.: A geometry for detection theory. Ph.D. thesis, Rice Unviersity (1992)Google Scholar
  7. 7.
    Ebert, S., Fritz, M., Schiele, B.: RALF: a reinforced active learning formulation for object class recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3626–3633 (2012)Google Scholar
  8. 8.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  9. 9.
    Fang, M., Li, Y., Cohn, T.: Learning how to active learn: a deep reinforcement learning approach. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 595–605 (2017)Google Scholar
  10. 10.
    Gal, Y., Islam, R., Ghahramani, Z.: Deep Bayesian active learning with image data. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1183–1192. JMLR. org (2017)Google Scholar
  11. 11.
    Gorriz, M., Carlier, A., Faure, E., Giro-i Nieto, X.: Cost-effective active learning for melanoma segmentation. arXiv preprint arXiv:1711.09168 (2017)
  12. 12.
    Guo, Y.: Active instance sampling via matrix partition. In: Advances in Neural Information Processing Systems, pp. 802–810 (2010)Google Scholar
  13. 13.
    Joshi, A.J., Porikli, F., Papanikolopoulos, N.: Multi-class active learning for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2372–2379. IEEE (2009)Google Scholar
  14. 14.
    Kasarla, T., Nagendar, G., Hegde, G., Balasubramanian, V., Jawahar, C.: Region-based active learning for efficient labeling in semantic segmentation. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1109–1118, January 2019Google Scholar
  15. 15.
    Konyushkova, K., Uijlings, J., Lampert, C.H., Ferrari, V.: Learning intelligent dialogs for bounding box annotation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018Google Scholar
  16. 16.
    Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report, Citeseer (2009)Google Scholar
  17. 17.
    Kuo, W., Häne, C., Yuh, E., Mukherjee, P., Malik, J.: Cost-sensitive active learning for intracranial hemorrhage detection. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018, Part III. LNCS, vol. 11072, pp. 715–723. Springer, Cham (2018). Scholar
  18. 18.
    Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: ICML Workshop on Challenges in Representation Learning (WREPL) (2013)Google Scholar
  19. 19.
    Lewis, D.D., Catlett, J.: Heterogeneous uncertainty sampling for supervised learning. In: Machine Learning Proceedings 1994, pp. 148–156. Elsevier (1994)Google Scholar
  20. 20.
    Lewis, D.D., Gale, W.A.: A sequential algorithm for training text classifiers. In: Croft, B.W., van Rijsbergen, C.J. (eds.) SIGIR 1994, pp. 3–12. Springer, Heidelberg (1994). Scholar
  21. 21.
    Li, X., Guo, Y.: Adaptive active learning for image classification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp. 859–866 (2013)Google Scholar
  22. 22.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Heidelberg (2016). Scholar
  23. 23.
    Liu, Z., Wang, J., Gong, S., Lu, H., Tao, D.: Deep reinforcement active learning for human-in-the-loop person re-identification. In: The IEEE International Conference on Computer Vision (ICCV), October 2019Google Scholar
  24. 24.
    Luo, W., Schwing, A., Urtasun, R.: Latent structured active learning. In: Advances in Neural Information Processing Systems, pp. 728–736 (2013)Google Scholar
  25. 25.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  26. 26.
    Mackowiak, R., Lenz, P., Ghori, O., Diego, F., Lange, O., Rother, C.: CEREALS - cost-effective region-based active learning for semantic segmentation. In: British Machine Vision Conference 2018, BMVC 2018, 3–6 September 2018. Northumbria University, Newcastle (2018)Google Scholar
  27. 27.
    Mahapatra, D., Bozorgtabar, B., Thiran, J.P., Reyes, M.: Efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network. In: Frangi, A., Schnabel, J., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, pp. 580–588. Springer, Heidelberg (2018). Scholar
  28. 28.
    Mayer, C., Timofte, R.: Adversarial sampling for active learning. arXiv preprint arXiv:1808.06671 (2018)
  29. 29.
    Nguyen, H.T., Smeulders, A.: Active learning using pre-clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 79. ACM (2004)Google Scholar
  30. 30.
    Rosenfeld, A., Zemel, R.S., Tsotsos, J.K.: The elephant in the room. CoRR abs/1808.03305 (2018)Google Scholar
  31. 31.
    Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: International Conference on Learning Representations (2018)Google Scholar
  32. 32.
    Settles, B.: Active learning. Synthesis Lect. Arti. Intell. Mach. Learn. 6(1), 1–114 (2012)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1070–1079. Association for Computational Linguistics (2008)Google Scholar
  34. 34.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)Google Scholar
  35. 35.
    Sinha, S., Ebrahimi, S., Darrell, T.: Variational adversarial active learning. In: The IEEE International Conference on Computer Vision (ICCV), October 2019Google Scholar
  36. 36.
    Wang, K., Zhang, D., Li, Y., Zhang, R., Lin, L.: Cost-effective active learning for deep image classification. IEEE Trans. Circ. Syst. Video Technol. 27(12), 2591–2600 (2017)CrossRefGoogle Scholar
  37. 37.
    Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3–4), 229–256 (1992)zbMATHGoogle Scholar
  38. 38.
    Woodward, M., Finn, C.: Active one-shot learning. In: NIPS Deep RL Workshop (2017)Google Scholar
  39. 39.
    Yang, L., Zhang, Y., Chen, J., Zhang, S., Chen, D.Z.: Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 399–407. Springer, Heidelberg (2017). Scholar
  40. 40.
    Yoo, D., Kweon, I.S.: Learning loss for active learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019Google Scholar
  41. 41.
    Yu, F., Koltun, V., Funkhouser, T.: Dilated residual networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 472–480 (2017)Google Scholar
  42. 42.
    Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., Darrell, T.: Bdd100k: a diverse driving video database with scalable annotation tooling. arXiv preprint arXiv:1805.04687 (2018)
  43. 43.
    Zhou, K., Qiao, Y., Xiang, T.: Deep reinforcement learning for unsupervised video summarization with diversity-representativeness reward (2018)Google Scholar
  44. 44.
    Zhu, J.J., Bento, J.: Generative adversarial active learning. arXiv preprint arXiv:1702.07956 (2017)

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.IIIT-DelhiNew DelhiIndia
  2. 2.Flixstock Inc.New DelhiIndia
  3. 3.Indian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations