Semi-Supervised Image Captioning by Adversarially Propagating Labeled Data

TL;DR

This paper introduces a semi-supervised image captioning framework that leverages large unpaired image and caption datasets through adversarial learning to improve captioning performance, especially when paired data is scarce.

Contribution

The paper proposes a novel adversarial semi-supervised learning approach that associates unpaired image and caption data, enhancing image captioning models' generalization capabilities.

Findings

Significant performance improvements on image captioning benchmarks.

Effective handling of out-of-task and web-crawled unpaired data.

Theoretically well-founded with favorable global optimal properties.

Abstract

We present a novel data-efficient semi-supervised framework to improve the generalization of image captioning models. Constructing a large-scale labeled image captioning dataset is an expensive task in terms of labor, time, and cost. In contrast to manually annotating all the training samples, separately collecting uni-modal datasets is immensely easier, e.g., a large-scale image dataset and a sentence dataset. We leverage such massive unpaired image and caption data upon standard paired data by learning to associate them. To this end, our proposed semi-supervised learning method assigns pseudo-labels to unpaired samples in an adversarial learning fashion, where the joint distribution of image and caption is learned. Our method trains a captioner to learn from a paired data and to progressively associate unpaired data. This approach shows noticeable performance improvement even in challenging scenarios including out-of-task data (i.e., relational captioning, where the target task is different from the unpaired data) and web-crawled data. We also show that our proposed method is theoretically well-motivated and has a favorable global optimal property. Our extensive and comprehensive empirical results both on (1) image-based and (2) dense region-based captioning datasets followed by comprehensive analysis on the scarcely-paired COCO dataset demonstrate the consistent effectiveness of our semisupervised learning method with unpaired data compared to competing methods.