  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.
