Few-shot Object Detection as a Semi-supervised Learning Problem

Abstract

This paper addresses the issue of dealing with few-shot learning settings in which different classes are annotated on different datasets. Each part of the data has exhaustive annotations for only one or a small set of classes, but not for others used in training. It is likely, that unannotated samples of a class exist, potentially impacting the gradient as negative samples. Because of this fact, we argue that few-shot learning is essentially a semi-supervised learning problem. We analyze how approaches from semi-supervised learning can be applied. In particular, the use of soft-sampling to weight the gradient based on overlap of detections and ground truth, and creating missing annotations using a preliminary detector are studied. The use of soft-sampling provides small but consistent improvements, at much lower computational effort than predicting additional annotations.