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• W4320921420 endingPage "110389" @default.
• W4320921420 startingPage "110389" @default.
• W4320921420 abstract "Supervised data are not always uncontroversial, especially for boundary detection tasks. Considering that we have a portrait, the face contour is naturally the most salient boundary. Interestingly, some people, but not everyone, regard their eyes or eyebrows as boundaries. This means that humans might have different understandings of the same thing, which leads to nondeterministic labels. In this paper, we propose a novel head function based on the Beta distribution for boundary detection. Different from learning the probability in the Bernoulli distribution, it introduces more abundant information. It can be viewed as the distribution of the former’s parameters and captures the uncertainty for binary classification problems. To this end, we employ the maximum likelihood and knowledge distillation as loss functions to train a deep network with the Beta head function. Moreover, we propose an efficient data processing method based on a recurrent voting strategy for merging these nondeterministic labels. In implementation, our Beta head function is lightweight and not limited to specific models. It can be seamlessly employed in many existing boundary detection networks without modifying their backbones. After introducing our Beta head function, the performances of three well-known boundary detection networks have been obviously improved. Multiple experiments indicate the effectiveness and the advantage of introducing uncertainty by the proposed Beta head function." @default.
• W4320921420 created "2023-02-16" @default.
• W4320921420 creator A5017517029 @default.
• W4320921420 creator A5061505268 @default.
• W4320921420 creator A5087615637 @default.
• W4320921420 date "2023-04-01" @default.
• W4320921420 modified "2023-09-23" @default.
• W4320921420 title "Beta network for boundary detection under nondeterministic labels" @default.
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• W4320921420 doi "https://doi.org/10.1016/j.knosys.2023.110389" @default.
• W4320921420 hasPublicationYear "2023" @default.
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