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Found 10 papers, 4 papers with code
Generalization of CNNs on Relational Reasoning with Bar Charts
We then test the generalization performance of CNNs on a classic relational reasoning task: estimating bar length ratios in a bar chart, by progressively perturbing the standard visualizations.
SPAC-Net: Rethinking Point Cloud Completion with Structural Prior
Point cloud completion aims to infer a complete shape from its partial observation.
ChartInsights: Evaluating Multimodal Large Language Models for Low-Level Chart Question Answering
To further explore the limitations of MLLMs in low-level ChartQA, we conduct experiments that alter visual elements of charts (e. g., changing color schemes, adding image noise) to assess their impact on the task effectiveness.
Mystique: Deconstructing SVG Charts for Layout Reuse
To facilitate the reuse of existing charts, previous research has examined how to obtain a semantic understanding of a chart by deconstructing its visual representation into reusable components, such as encodings.
An Intelligent Model for Solving Manpower Scheduling Problems
In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results.
Scribble-Supervised Semantic Segmentation by Uncertainty Reduction on Neural Representation and Self-Supervision on Neural Eigenspace
Scribble-supervised semantic segmentation has gained much attention recently for its promising performance without high-quality annotations.
Implicit Multidimensional Projection of Local Subspaces
We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation.
Deep-Learning Inversion of Seismic Data
We propose a new method to tackle the mapping challenge from time-series data to spatial image in the field of seismic exploration, i. e., reconstructing the velocity model directly from seismic data by deep neural networks (DNNs).
Progressive Data Science: Potential and Challenges
We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
DifNet: Semantic Segmentation by Diffusion Networks
Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions.
