Meta-DETR: Image-Level Few-Shot Object Detection with Inter-Class Correlation Exploitation

Understand

Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.

• Despite its success, the said paradigm is constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes.
• Such limitations hinder the generalization of base-class knowledge for the detection of novel-class objects.
• In this work, we design Meta-DETR, a novel few-shot detection framework that incorporates correlational aggregation for meta-learning into DETR detection frameworks.