方法证据记录
Few-Shot Object Detection
Few-Shot Object Detection (FSOD) is a meta-learning approach that enables detecting novel object classes from only a few annotated examples. Unlike standard object detection requiring hundreds of labeled instances per class, FSOD learns to quickly adapt detection models to new object categories by leveraging knowledge from base categories.
源记录
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Few-Shot Object Detection with Contrastive Learning
分类方法记录 · ml-model / deep-learning
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