A review on anchor assignment and sampling heuristics in deep learning-based object detection.

• A detailed literature review for the existing methods about anchor assignment and sample sampling is investigated and analyzed in a taxonomy. We identify a definition of each component, challenging problems in existing methods and their solution, and a comprehensive comparison of all methods. To the best of our knowledge, there is no prior work discussing these problems in object detection literature. • Open research direction in both anchor assignment and sampling heuristics is discussed. Based on these directions, we hope the object detection community can identify the current status of object detection methods to propose better solutions to anchor assignment and sampling heuristics. • We review the state-of-the-art object detection in the last two years that complement existing surveys. We also provide paper literature related to the above problems via the repository webpage. Deep learning-based object detection is a fundamental but challenging problem in computer vision field, has attracted a lot of study in recent years. State-of-the-art object detection methods rely on the selection of positive samples and negative samples, i.e., called sample assignment, and the definition of a useful set for training, i.e., called sample sampling heuristics. This paper presents a comprehensive review of the advanced anchor assignment and sampling approaches in deep learning-based object detection. Each problem is classified and analyzed systematically. According to the problem-based taxonomy, we identify the advantages and disadvantages of each problem in-depth and present open issues regarding the current methods. Furthermore, this paper also reviews the new trends in solving object detection that has not been discussed during the last two years. To track the latest research, a webpage related to the above problems is provided, which is available at https://github.com/VoXuanThuy/ObjectDetectionReview. [ABSTRACT FROM AUTHOR]