Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation

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Abstract

In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model's accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model's accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.

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teaser: In both scatterplots shown here, the percentage of images with correctly predicted class labels (visualized as border color) is over 90%. We found that users can estimate these accuracies fairly well. Image complexity impacts overall performance, but the layout has very little effect on users’ estimations. teaser:In both scatterplots shown here, the percentage of images with correctly predicted class labels (visualized as border color) is over 90%. We found that users can estimate these accuracies fairly well. Image complexity impacts overall performance, but the layout has very little effect on users’ estimations.

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BibTeX

@inproceedings{grossmann-2021-layout,
  title =      "Does the Layout Really Matter? A Study on Visual Model
               Accuracy Estimation",
  author =     "Nicolas Grossmann and J\"{u}rgen Bernard and Michael
               Sedlmair and Manuela Waldner",
  year =       "2021",
  abstract =   "In visual interactive labeling, users iteratively assign
               labels to data items until the machine model reaches an
               acceptable accuracy. A crucial step of this process is to
               inspect the model's accuracy and decide whether it is
               necessary to label additional elements. In scenarios with no
               or very little labeled data, visual inspection of the
               predictions is required. Similarity-preserving scatterplots
               created through a dimensionality reduction algorithm are a
               common visualization that is used in these cases. Previous
               studies investigated the effects of layout and image
               complexity on tasks like labeling. However, model evaluation
               has not been studied systematically. We present the results
               of an experiment studying the influence of image complexity
               and visual grouping of images on model accuracy estimation.
               We found that users outperform traditional automated
               approaches when estimating a model's accuracy. Furthermore,
               while the complexity of images impacts the overall
               performance, the layout of the items in the plot has little
               to no effect on estimations.",
  month =      oct,
  booktitle =  "IEEE VIS",
  event =      "IEEE Visualization Conference (VIS)",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/grossmann-2021-layout/",
}