FEW-SHOT OBJECT DETECTION WITH LOCAL CORRESPONDENCE RPN and ATTENTIVE HEAD

Existing object detection methods rely heavily on a large number of annotated bounding boxes, which is expensive to collect. In this paper, we propose a novel few-shot object detection method named GCN-FSOD. Intending to find informal local correspondence to fully explore cues of novel classes, we propose the local correspondence RPN (lcRPN) and the attentive detection head for few-shot detection. Taking features from the support-query image pair as inputs, lcRPN generates region proposals by mining fine-grained local correspondence with the help of GCNs. Then the proposed attentive head performs precise detection. We conduct extensive experiments on the wildly adopted MS-COCO benchmark. The proposed GCN-FSOD brings significant performance gains and outperforms the state-of-the-art by a large margin (1.7% mAP for 10-shot).