A Survey of Self-Supervised and Few-Shot Object Detection

• URL: http://arxiv.org/abs/2110.14711v1
• Date: Wed, 27 Oct 2021 18:55:47 GMT
• Title: A Survey of Self-Supervised and Few-Shot Object Detection
• Authors: Gabriel Huang, Issam Laradji, David Vazquez, Simon Lacoste-Julien, Pau Rodriguez
• Abstract summary: Self-supervised methods aim at learning representations from unlabeled data which transfer well to downstream tasks such as object detection. Few-shot object detection is about training a model on novel (unseen) object classes with little data. In this survey, we review and characterize the most recent approaches on few-shot and self-supervised object detection.
• Score: 19.647681501581225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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