Visual Probing and Correction of Object Recognition Models with Interactive user feedback

TL;DR

This paper visualizes uncertainties in object recognition models and introduces a user feedback-based correction process, demonstrated on VAST 2020 Mini-Challenge 2 data, aiming to improve model reliability in critical applications.

Contribution

It proposes a novel visualization and correction method for object recognition uncertainties using interactive user feedback, enhancing model accuracy in sensitive applications.

Findings

Uncertainty visualization helps identify model weaknesses.

User feedback effectively corrects recognition errors.

Demonstrated improvements on VAST 2020 Mini-Challenge 2 dataset.

Abstract

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.