# Few-Example Object Detection with Model Communication

## TL;DR

This paper introduces a novel few-example object detection method that iteratively improves model training using high-confidence samples and multiple detection models, achieving competitive results with minimal labeled data.

## Contribution

The proposed approach leverages multiple models and iterative sample selection to enhance detection accuracy with very few labeled examples, outperforming single-model baselines.

## Key findings

- Outperforms baseline methods with as few as 3-4 samples per category.
- Effective use of multiple models improves sample quality and detection performance.
- Achieves competitive results compared to state-of-the-art weakly-supervised methods.

## Abstract

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.