Interactive Attribution-based Explanations for Image Segmentation
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Explanations of deep neural networks (DNNs) give users a better understanding of the inner workings and generalizability of a network. While the majority of research focuses on explanations for classification networks, in this work we focus on explainability for image segmentation networks. As a first contribution, we introduce a lightweight framework that allows generalizing certain attribution-based explanations, originally developed for classification networks, to also work for segmentation networks. The second contribution is a web-based tool that utilizes this framework and allows users to interactively explore segmentation networks. We demonstrate the approach using a self-trained mushroom segmentation network.
Description
CCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Image segmentation
@inproceedings{10.2312:evp.20221130,
booktitle = {EuroVis 2022 - Posters},
editor = {Krone, Michael and Lenti, Simone and Schmidt, Johanna},
title = {{Interactive Attribution-based Explanations for Image Segmentation}},
author = {Humer, Christina and Elharty, Mohamed and Hinterreiter, Andreas and Streit, Marc},
year = {2022},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-185-4},
DOI = {10.2312/evp.20221130}
}