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A Comparative Review of Recent Few-Shot Object Detection Algorithms
[article]
2021
arXiv
pre-print
Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data and the urgent demands to cut costs of data collection and annotation. Recently, some studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions. This survey provides a comprehensive overview from current
arXiv:2111.00201v1
fatcat:preckuym7zarndm4yjc7n4k2oi