Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning

TL;DR

This paper introduces a novel approach for text-only image captioning that uses synthetic image-text pairs generated by a pre-trained text-to-image model to improve cross-modal alignment and achieve state-of-the-art results.

Contribution

It proposes a new method leveraging synthetic pairs and pseudo features to bridge modality gaps and unify training and inference in text-only image captioning.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively bridges modality gap between text and image features.

Enhances cross-modal alignment using synthetic image-text pairs.

Abstract

Although image captioning models have made significant advancements in recent years, the majority of them heavily depend on high-quality datasets containing paired images and texts which are costly to acquire. Previous works leverage the CLIP's cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. However, not only does a modality gap exist between CLIP text and image features, but a discrepancy also arises between training and inference due to the unavailability of real-world images, which hinders the cross-modal alignment in text-only captioning. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space. Furthermore, textual information is gathered to represent image features, resulting in the image features with various semantics and the bridged modality gap. To unify training and inference, synthetic image features would serve as the training prefix for the language decoder, while real images are used for inference. Additionally, salient objects in images are detected as assistance to enhance the learning of modality alignment. Experimental results demonstrate that our method obtains the state-of-the-art performance on benchmark datasets.