  Few-shot object detection has been extensively investigated by incorporating
meta-learning into region-based detection frameworks. Despite its success, the
said paradigm is still 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, which (i) is the first image-level few-shot detector, and
(ii) introduces a novel inter-class correlational meta-learning strategy to
capture and leverage the correlation among different classes for robust and
accurate few-shot object detection. Meta-DETR works entirely at image level
without any region proposals, which circumvents the constraint of inaccurate
proposals in prevalent few-shot detection frameworks. In addition, the
introduced correlational meta-learning enables Meta-DETR to simultaneously
attend to multiple support classes within a single feedforward, which allows to
capture the inter-class correlation among different classes, thus significantly
reducing the misclassification over similar classes and enhancing knowledge
generalization to novel classes. Experiments over multiple few-shot object
detection benchmarks show that the proposed Meta-DETR outperforms
state-of-the-art methods by large margins. The implementation codes are
available at https://github.com/ZhangGongjie/Meta-DETR.
