Enhancing few-shot object detection through pseudo-label mining

Pablo Garcia-Fernandez, Daniel Cores, Manuel Mucientes

Abstract: Highlights•We propose xPAND, a mining pipeline that generates high-quality diverse pseudo-labels.•xPAND refines a raw set of pseudo-labels using class and box confirmation modules.•Class module removes misclassified objects by comparing them with expected prototypes.•Box Confirmation estimates IoU to discard inadequately framed objects.•Results show that xPAND sets a new state of the art for both MS-COCO and VOC datasets.