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Found 5 papers, 0 papers with code
An Image-based Typology for Visualization
In addition to the visualization typology from images, we provide a dataset of 6, 833 tagged images and an online tool that can be used to explore and analyze the large set of labeled images.
ClustML: A Measure of Cluster Pattern Complexity in Scatterplots Learnt from Human-labeled Groupings
We propose a new VQM for visual grouping patterns in scatterplots, called ClustML, which is trained on previously collected human subject judgments.
Document Domain Randomization for Deep Learning Document Layout Extraction
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e. g. t-SNE, UMAP, etc.).
VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
We present the VIS30K dataset, a collection of 29, 689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).
