Unsupervised Object Discovery: A Comprehensive Survey and Unified Taxonomy
It provides a comprehensive overview for researchers and practitioners in computer vision to navigate and understand unsupervised object discovery methods, though it is incremental as a survey.
This survey tackles the challenge of synthesizing the extensive and diverse literature on unsupervised object discovery by conducting an in-depth exploration and systematically categorizing approaches based on tasks and techniques, while also highlighting issues with evaluation protocols.
Unsupervised object discovery is commonly interpreted as the task of localizing and/or categorizing objects in visual data without the need for labeled examples. While current object recognition methods have proven highly effective for practical applications, the ongoing demand for annotated data in real-world scenarios drives research into unsupervised approaches. Furthermore, existing literature in object discovery is both extensive and diverse, posing a significant challenge for researchers that aim to navigate and synthesize this knowledge. Motivated by the evidenced interest in this avenue of research, and the lack of comprehensive studies that could facilitate a holistic understanding of unsupervised object discovery, this survey conducts an in-depth exploration of the existing approaches and systematically categorizes this compendium based on the tasks addressed and the families of techniques employed. Additionally, we present an overview of common datasets and metrics, highlighting the challenges of comparing methods due to varying evaluation protocols. This work intends to provide practitioners with an insightful perspective on the domain, with the hope of inspiring new ideas and fostering a deeper understanding of object discovery approaches.