| Autor: |
Nguyen, Huyen N., Gonzalez, Jake, Guo, Jian, Nguyen, Ngan V. T., Dang, Tommy |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Conference on Visual Analytics Science and Technology (VAST) 2020 |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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