Characterizing Uncertainty in the Visual Text Analysis Pipeline

2022/09/22 by Pantea Haghighatkhah, Haghighatkhah, Pantea, Mennatallah El‐Assady +11 · 1 citation
Computer Science · #Data Visualization and Analytics #Video Analysis and Summarization

paper · pdf · doi:10.48550/arxiv.2209.13498

Abstract

Current visual text analysis approaches rely on sophisticated processing pipelines. Each step of such a pipeline potentially amplifies any uncertainties from the previous step. To ensure the comprehensibility and interoperability of the results, it is of paramount importance to clearly communicate the uncertainty not only of the output but also within the pipeline. In this paper, we characterize the sources of uncertainty along the visual text analysis pipeline. Within its three phases of labeling, modeling, and analysis, we identify six sources, discuss the type of uncertainty they create, and how they propagate.

Cited by

• Beyond Quantification: Navigating Uncertainty in Professional AI Systems

Related

• Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Models
• Maybe, Maybe Not: A Survey on Uncertainty in Visualization
• Handling uncertainty in a system for text-symbol context analysis
• Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
• Why Authors Don't Visualize Uncertainty