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We propose a hybrid architecture composed of a fully convolutional network (FCN) and a Dempster-Shafer layer for image semantic segmentation.
Annals of Mathematical Statistics 38
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Machine Vision and Applications 27
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arXiv preprint arXiv:1608.05442 (2016)
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International journal of computer vision 111
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: A retrospective · 2015
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Lian, C., Ruan, S., Denœux, T.: An evidential classifier based on feature selection and two-step classification strategy · 2015
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In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
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In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1520–1528 (2015)
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Yoon, Y., Jeon, H.G., Yoo, D., Lee, J.Y., So Kweon, I.: Learning a deep convolutional network for light-field image super-resolution · 2015
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In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3213–3223 (2016)
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Knowledge-Based Systems 142
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Denoeux, T.: Decision-making with belief functions: a review · 2019
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Denœux, T.: Logistic regression, neural networks and Dempster-Shafer theory: A new perspective · 2019
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Denœux, T., Kanjanatarakul, O., Sriboonchitta, S.: A new evidential k-nearest neighbor rule based on contextual discounting with partially supervised learning · 2019
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In: Processing of the 13th international conference on Scalable Uncertainty Management, pp. 368–381. Springer International Publishing, Cham (2019)
Tong, Z., Xu, P., Denœux, T.: ConvNet and Dempster-Shafer theory for object recognition · 2019
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In: A Guided Tour of Artificial Intelligence Research, vol. 1, chap. 4, pp. 119–150. Springer Verlag (2020)
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In: International Conference on Knowledge Science, Engineering and Management, pp. 427–437. Springer (2020)
Yuan, B., Yue, X., Lv, Y., Denoeux, T.: Evidential deep neural networks for uncertain data classification · 2020
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Knowledge-Based Systems 214
Ma, L., Denœux, T.: Partial classification in the belief function framework · 2021
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