Unsupervised Annotation and Detection of Novel Objects Using Known Objectness

Abstract

The paper proposes a new approach to detecting and annotating novel objects in images that are not precisely part of a training dataset. The ability to detect novel objects is essential in computer vision, enabling machines to recognise objects that have not been seen before. Current models often fail to detect novel objects as they rely on predefined categories in the training data. Our approach overcomes this limitation by leveraging a large and diverse dataset of objects obtained through web scraping. We extract features using a backbone network and perform clustering to remove redundant data. The resulting dataset is used to retrain the object detection models to obtain results. The method provides deep insights into the effect of clustering and data redundancy removal on performance. Overall, the work contributes to the field of object detection by providing a new approach for detecting novel objects. The method has the potential to be applied to a variety of real-world CV applications. � 2024 by SCITEPRESS � Science and Technology Publications, Lda.