  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.
