Abstract: Few-Shot Object Detection (FSOD) aims to detect the objects of novel classes using only a few manually annotated samples. With the few novel class samples, learning the inter-class ...
Abstract: Open-world class-agnostic object detection aims to localize all objects in images regardless of whether their categories are known during training. Most existing studies focus on unannotated ...
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