Semantic bottleneck for computer vision tasks

By Max Bucher, Stéphane Herbin, Frédéric Jurie. 2018

18 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Concept Bottleneck Models (2020, background): “More recently, deep neural networks with concept bottlenecks have re-emerged as targeted tools for solving particular tasks, e.g., Fauw et al. (2018) for retinal disease diagnosis, Yi et al. (2018) for visual question-answering, and Bucher et al. (2018) for content-based image retrieval.”