Self-Distillation for Few-Shot Image Captioning

The development of large-scale image-captioning datasets is expensive, while the abundance of unpaired images and text corpus can potentially help reduce the efforts of manual annotation. In this paper, we study the few-shot image captioning problem that only requires a small amount of annotated image-caption pairs. We propose an ensemble-based self-distillation method that allows image captioning models to be trained with unpaired images and captions. The ensemble consists of multiple base models trained with different data samples in each iteration. For learning from unpaired images, we generate multiple pseudo captions with the ensemble and allocate different weights according to their confidence levels. For learning from unpaired captions, we propose a simple yet effective pseudo feature generation method based on Gradient Descent. The pseudo captions and pseudo features from the ensemble are used to train the base models in future iterations. The proposed method is general over different image captioning models and datasets. Our experiments demonstrate significant performance improvements and meaningful captions generated with only 1% of paired training data.