Huang Gabriel, Laradji Issam, Vazquez David, Lacoste-Julien Simon, Rodriguez Pau
IEEE Trans Pattern Anal Mach Intell. 2023 Apr;45(4):4071-4089. doi: 10.1109/TPAMI.2022.3199617. Epub 2023 Mar 7.
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: https://gabrielhuang.github.io/fsod-survey/.
标注数据通常既昂贵又耗时，尤其是对于目标检测和实例分割等任务而言，这些任务需要对图像进行密集标注。虽然少样本目标检测是指在很少的数据上针对新的（未见过的）目标类别训练模型，但它仍然需要在许多已标注的基础（见过的）类别的示例上进行预先训练。另一方面，自监督方法旨在从未标注数据中学习表征，这些表征能很好地迁移到诸如目标检测等下游任务中。将少样本和自监督目标检测相结合是一个很有前景的研究方向。在本次综述中，我们回顾并描述了少样本和自监督目标检测的最新方法。然后，我们给出主要结论并讨论未来的研究方向。项目页面：https://gabrielhuang.github.io/fsod-survey/ 。
