VisMCA: A Visual Analytics System for Misclassification Correction and Analysis. VAST Challenge 2020, Mini-Challenge 2 Award: Honorable Mention for Detailed Analysis of Patterns of Misclassification
This addresses the problem of improving object detection accuracy for users in visual analytics by providing a tool for manual correction and pattern analysis, though it is incremental as it builds on existing visual analytics methods.
The paper presents VisMCA, an interactive visual analytics system that helps users correct misclassifications in object detection results and analyze underlying patterns, winning an Honorable Mention in the VAST Challenge 2020.
This paper presents VisMCA, an interactive visual analytics system that supports deepening understanding in ML results, augmenting users' capabilities in correcting misclassification, and providing an analysis of underlying patterns, in response to the VAST Challenge 2020 Mini-Challenge 2. VisMCA facilitates tracking provenance and provides a comprehensive view of object detection results, easing re-labeling, and producing reliable, corrected data for future training. Our solution implements multiple analytical views on visual analysis to offer a deep insight for underlying pattern discovery.