Visual Probing and Correction of Object Recognition Models with Interactive user feedback
This work addresses the problem of improving the reliability of object recognition models for real-world, sensitive applications like autonomous driving and cancer detection, which require high accuracy and minimal uncertainty.
The paper visualizes uncertainties in object recognition models and proposes a correction process using user feedback. The approach is demonstrated on data from the VAST 2020 Mini-Challenge 2.
With the advent of state-of-the-art machine learning and deep learning technologies, several industries are moving towards the field. Applications of such technologies are highly diverse ranging from natural language processing to computer vision. Object recognition is one such area in the computer vision domain. Although proven to perform with high accuracy, there are still areas where such models can be improved. This is in-fact highly important in real-world use cases like autonomous driving or cancer detection, that are highly sensitive and expect such technologies to have almost no uncertainties. In this paper, we attempt to visualise the uncertainties in object recognition models and propose a correction process via user feedback. We further demonstrate our approach on the data provided by the VAST 2020 Mini-Challenge 2.