Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image. While few-shot object detection is about training a model on novel (unseen) object classes with little data, it still requires prior training on many labeled examples of base (seen) classes. On the other hand, self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Combining few-shot and self-supervised object detection is a promising research direction. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection. Then, we give our main takeaways and discuss future research directions. Project page at https://gabrielhuang.github.io/fsod-survey/
@article{arxiv.2110.14711,
title = {A Survey of Self-Supervised and Few-Shot Object Detection},
author = {Gabriel Huang and Issam Laradji and David Vazquez and Simon Lacoste-Julien and Pau Rodriguez},
journal= {arXiv preprint arXiv:2110.14711},
year = {2022}
}
Comments
To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence. Awesome Few-Shot Object Detection (Leaderboard) at https://github.com/gabrielhuang/awesome-few-shot-object-detection