Abstract
This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language, while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection.
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References
Das, A., et al.: Visual dialog. In: CVPR (2016)
Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an approach to evaluating interpretability of ML. arXiv:1806.00069 (2018)
Doshi-Velez, F., Kim, B.: A roadmap for a rigorous science of interpretability. arXiv:1702.08608 (2017)
Biran, O., Cotton, C.: Explanation and justification in ML: a survey. In: IJCAI (2017)
Ras, G., Haselager, P., van Gerven, M.: Explanation methods in deep learning: users, values, concerns and challenges. arXiv:1803.07517 (2018)
Zhang, P., Wang, J., Farhadi, A., Hebert, M., Parikh, D.: Predicting failures of vision systems. In: CVPR (2014)
Lampert, C.H., Nickisch, H., Harmeling, S.: Attribute-based classification for zero-shot visual object categorization. TPAMI (2014)
Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. TPAMI (2017)
Lu, J., Xiong, C., Parikh, D., Socher, R.: Knowing when to look: adaptive attention via a visual sentinel for image captioning. In: CVPR (2017)
Gordo, A., Larlus, D.: Beyond instance-level image retrieval: leveraging captions to learn a global visual representation for semantic retrieval. In: CVPR (2017)
Dai, B., Lin, D., Urtasun, R., Fidler, S.: Towards diverse and natural image descriptions via a conditional GAN. In: CVPR (2017)
Dai, B., Lin, D.: Contrastive learning for image captioning. In: NIPS (2017)
Seo, P.H., Lehrmann, A., Han, B., Sigal, L.: Visual reference resolution using attention memory for visual dialog. In: NIPS (2017)
Lu, J., Kannan, A., Yang, J., Parikh, D., Batra, D.: Best of both worlds: transferring knowledge from discriminative learning to a generative visual dialog model. In: NIPS (2017)
Malinowski, M., Rohrbach, M., Fritz, M.: Ask your neurons: a neural-based approach to answering questions about images. In: ICCV (2015)
Lin, X., Parikh, D.: Leveraging visual question answering for image-caption ranking. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 261–277. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_17
Zhu, Y., Groth, O., Bernstein, M., Fei-Fei, L.: Visual7w: grounded question answering in images. In: CVPR (2016)
Jabri, A., Joulin, A., van der Maaten, L.: Revisiting VQA baselines. In: ECCV (2016)
Wu, Q., Wang, P., Shen, C., Dick, A., van den Hengel, A.: Ask me anything: free-form visual question answering based on knowledge from external sources. In: CVPR (2016)
Lipton, Z.C.: The mythos of model interpretability. In: ICML (2016)
Doran, D., Schulz, S., Besold, T.R.: What does explainable AI really mean? A new conceptualization of perspectives. arXiv:1710.00794 (2017)
Hohman, F.M., Kahng, M., Pienta, R., Chau, D.H.: Visual analytics in deep learning: an interrogative survey for the next frontiers. In: TVCG (2018)
Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., Giannotti, F.: A survey of methods for explaining black box models. arXiv:1802.01933 (2018)
Zhang, Q., Yang, Y., Wu, Y.N., Zhu, S.C.: Interpreting CNNs via decision trees. arXiv:1802.00121 (2018)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: ICCV (2017)
Rajani, N.F., Mooney, R.J.: Using explanations to improve ensembling of visual question answering systems. In: IJCAI (2017)
Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Sign. Process. Rev. J. (2018)
Hendricks, L.A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., Darrell, T.: Generating visual explanations. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_1
Park, D.H., et al.: Multimodal explanations: justifying decisions and pointing to the evidence. In: CVPR (2018)
Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: CVPR (2015)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Dosovitskiy, A., Brox, T.: Inverting visual representations with convolutional networks. In: CVPR (2016)
Olah, C., et al.: The building blocks of interpretability. Distill (2018)
Zhang, Q., Cao, R., Shi, F., Wu, Y.N., Zhu, S.C.: Interpreting CNN knowledge via an explanatory graph. arXiv:1708.01785 (2017)
Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: CVPR (2017)
Kindermans, P.J., et al.: The (un)reliability of saliency methods. arXiv:1711.00867 (2017)
Serban, I.V., et al.: Generating factoid questions with recurrent neural networks: the 30M factoid question-answer corpus. In: ACL (2016)
Li, Y., Huang, C., Tang, X., Change Loy, C.: Learning to disambiguate by asking discriminative questions. In: CVPR (2017)
Mostafazadeh, N., Misra, I., Devlin, J., Mitchell, M., He, X., Vanderwende, L.: Generating natural questions about an image. In: ACL (2016)
Das, A., Kottur, S., Moura, J.M.F., Lee, S., Batra, D.: Learning cooperative visual dialog agents with deep reinforcement learning. In: ICCV 2017 (2017)
Ganju, S., Russakovsky, O., Gupta, A.: What’s in a question: using visual questions as a form of supervision. In: CVPR (2017)
Zhu, Y., Lim, J.J., Fei-Fei, L.: Knowledge acquisition for visual question answering via iterative querying. In: CVPR (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2014)
Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV (2015)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. (1997)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)
Antol, S., et al.: VQA: visual question answering. In: ICCV (2015)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR (2014)
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Bucher, M., Herbin, S., Jurie, F. (2019). Semantic Bottleneck for Computer Vision Tasks. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11362. Springer, Cham. https://doi.org/10.1007/978-3-030-20890-5_44
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