Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector

TL;DR

This paper introduces a novel few-shot object detection network that leverages Attention-RPN, Multi-Relation Detector, and Contrastive Training to detect unseen object categories with minimal examples, achieving state-of-the-art results.

Contribution

The paper presents a new few-shot object detection framework with specialized modules and a new dataset, enabling detection of unseen categories without additional training.

Findings

Achieved state-of-the-art performance on few-shot detection benchmarks.

Developed a new dataset with 1000 categories for training and evaluation.

Demonstrated the effectiveness of Attention-RPN and Multi-Relation Detector modules.

Abstract

Conventional methods for object detection typically require a substantial amount of training data and preparing such high-quality training data is very labor-intensive. In this paper, we propose a novel few-shot object detection network that aims at detecting objects of unseen categories with only a few annotated examples. Central to our method are our Attention-RPN, Multi-Relation Detector and Contrastive Training strategy, which exploit the similarity between the few shot support set and query set to detect novel objects while suppressing false detection in the background. To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations. To the best of our knowledge, this is one of the first datasets specifically designed for few-shot object detection. Once our few-shot network is trained, it can detect objects of unseen categories without further training or fine-tuning. Our method is general and has a wide range of potential applications. We produce a new state-of-the-art performance on different datasets in the few-shot setting. The dataset link is https://github.com/fanq15/Few-Shot-Object-Detection-Dataset.