Machine learningDeep Learning, Object Detection, Meta-Learning
少样本目标检测
少样本目标检测（FSOD）是一种元学习方法，能够仅从少量标注示例中检测新颖的目标类别。标准的物体检测需要每个类别有数百个标注实例，而FSOD通过利用基础类别的知识，学会快速地将检测模型适应于新的物体类别。
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来源
- Wang, X., Huang, T. E., Darrell, T., Gonzalez, J. E., & Yu, F. (2020). Few-shot object detection with attention-RPN and multi-relation detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9050-9059). link ↗
如何引用本页
ScholarGate. (2026, June 3). Few-Shot Object Detection with Contrastive Learning. ScholarGate. https://scholargate.app/zh/deep-learning/few-shot-object-detection
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