论文标题
具有交互式用户反馈的对象识别模型的视觉探测和校正
Visual Probing and Correction of Object Recognition Models with Interactive user feedback
论文作者
论文摘要
随着最先进的机器学习和深度学习技术的出现，一些行业正朝着该领域发展。从自然语言处理到计算机视觉，此类技术的应用非常多样化。对象识别是计算机视觉域中的一个这样的领域。尽管被证明可以很高的精度执行，但仍有一些可以改善此类模型的领域。这在现实世界中非常重要的情况非常重要，例如自主驾驶或癌症检测，这些案例非常敏感，并且期望这种技术几乎没有不确定性。在本文中，我们试图可视化对象识别模型中的不确定性，并通过用户反馈提出更正过程。我们进一步证明了我们在2020年大型迷你挑战2提供的数据上的方法。
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.
