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• W2899914520 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." @default.
• W2899914520 created "2018-11-16" @default.
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• W2899914520 date "2019-01-01" @default.
• W2899914520 modified "2023-09-25" @default.
• W2899914520 title "Semantic Bottleneck for Computer Vision Tasks" @default.
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• W2899914520 doi "https://doi.org/10.1007/978-3-030-20890-5_44" @default.
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