Meta-SSD: Towards Fast Adaptation for Few-Shot Object Detection With Meta-Learning

Abstract

The state-of-the-art object detection frameworks require the training on large-scale datasets, which is the crux of the present dilemma: overfitting or degrading performance with insufficient samples and time-consuming training process. On the basis of meta-learning, this paper proposes a generalized Few-Shot Detection (FSD) framework to overcome the above drawbacks of the current advances in object detection. The proposed framework consists of a meta-learner and an object detector. It can learn the general knowledge and proper fast adaptation strategies across many tasks. The meta-learner can teach the detector how to learn from few examples in just one updating step. Here, the object detector can be any supervised learning detection models in theory. Specifically, the proposed FSD framework employs Single-Shot MultiBox Detector (SSD) as the object detector in this paper, thus called Meta-SSD. Besides, a novel benchmark is constructed from Pascal VOC dataset for training and evaluation of meta-learning FSD. Experiments show that the Meta-SSD yields a promising result for FSD. Furthermore, the properties of Meta-SSD is analyzed. This paper can serve as a strong baseline and provide some inspiration for meta-learning FSD.