Faster-LTN: a neuro-symbolic, end-to-end object detection architecture

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2010

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Zhu, Y., Fathi, A., Fei-Fei, L.: Reasoning about object affordances in a knowledge base representation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) European conference on computer vision – ECCV 2014. pp. 408–424 (2014)

2014

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Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: Detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1971–1978 (2014)

2014

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Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) European conference on computer vision – ECCV 2016. pp. 852–869. Cham (2016)

2016

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Donadello, I., Serafini, L., Garcez, A.D.: Logic tensor networks for semantic image interpretation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. p. 1596–1602. AAAI Press (2017)

2017

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Besold, T.R., Garcez, A., Bader, S., Bowman, H., Domingos, P.M., Hitzler, P., Kühnberger, K.U., Lamb, L., Lowd, D., Lima, P., Penning, L., Pinkas, G., Poon, H., Zaverucha, G.: Neural-symbolic learning and reasoning: A survey and interpretation. ArXiv abs/1711.03902

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2017

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Marino, K., Salakhutdinov, R., Gupta, A.: The more you know: Using knowledge graphs for image classification. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 20–28 (2017)

2017

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Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39

2017

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Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 2999–3007 (2017)

2017

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Yi, K., Wu, J., Gan, C., Torralba, A., Kohli, P., Tenenbaum, J.B.: Neural-symbolic vqa: Disentangling reasoning from vision and language understanding. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. p. 1039–1050. Curran Associates Inc. (2018)

2018

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Garcez, A., Gori, M., Lamb, L., Serafini, L., Spranger, M., Tran, S.: Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. FLAP 6

2019

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Raedt, L.d., Dumančić, S., Manhaeve, R., Marra, G.: From statistical relational to neuro-symbolic artificial intelligence. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 4943–4950 (2020)

2020

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Badreddine, S., Garcez, A.d., Serafini, L., Spranger, M.: Logic tensor networks. ArXiv abs/2012.13635

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2020

Later among the works it cites.

Shanahan, M., Nikiforou, K., Creswell, A., Kaplanis, C., Barrett, D., Garnelo, M.: An explicitly relational neural network architecture. In: Proceedings of the 37th International Conference on Machine Learning. vol. 119, pp. 8593–8603. PMLR (2020)

2020

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