Survey of methods for detecting new objects with only a few examples
Survey of methods for detecting new objects with only a few examples
Few-Shot Object Detection: A Survey
arXiv paper abstract https://arxiv.org/abs/2112.11699v1
arXiv PDF paper https://arxiv.org/pdf/2112.11699v1.pdf
Humans are able to learn to recognize new objects even from a few examples.
In contrast, training deep-learning-based object detectors requires huge amounts of annotated data.
To avoid the need to acquire and annotate these huge amounts of data, few-shot object detection aims to learn from few object instances of new categories in the target domain.
In this survey, … provide an overview of the state of the art in few-shot object detection.
… categorize approaches according to their training scheme and architectural layout.
For each type of approaches, … describe the general realization as well as concepts to improve the performance on novel categories.
… introduce commonly used datasets and their evaluation protocols and analyze reported benchmark results. …
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
LinkedIn https://www.linkedin.com/in/morris-lee-47877b7b
