**arXiv ID:** 1811.02234 **Authors:** Maxime Bucher, Stéphane Herbin, Frédéric Jurie **Published:** 2018-11-06T09:01:02Z **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...
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# Semantic bottleneck for computer vision tasks
**arXiv ID:** 1811.02234
**Authors:** Maxime Bucher, Stéphane Herbin, Frédéric Jurie
**Published:** 2018-11-06T09:01:02Z
**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.
## Skill Description
This skill is generated from the arXiv paper: Semantic bottleneck for computer vision tasks (1811.02234).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:1811.02234](http://arxiv.org/abs/1811.02234v1)
