Few-Shot Object Detection via Synthetic Features with Optimal Transport

TL;DR

This paper introduces a novel few-shot object detection method that uses a generator trained with optimal transport loss on base classes to generate synthetic data for novel classes, improving detection performance.

Contribution

It proposes a generator trained with optimal transport loss on base classes to synthesize data for novel classes, enhancing few-shot detection capabilities.

Findings

Outperforms state-of-the-art on benchmark datasets

Effective synthetic data generation for novel classes

Improved detection accuracy in few-shot scenarios

Abstract

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available.