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Survey of training object detectors with limited data or unlabeled data

Survey of training object detectors with limited data or unlabeled data


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




Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation


... few-shot object detection is about training a model on novel (unseen) object classes with little data ... requires prior training on many labeled examples of base (seen) classes.


... self-supervised methods aim at learning representations from unlabeled data which transfer well to ... object detection.


Combining few-shot and self-supervised object detection is a promising research direction.


... review and characterize the most recent approaches on few-shot and self-supervised object detection. ...



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