PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting

TL;DR

This paper introduces PiTL, a novel weakly-supervised vision-language pre-training method that leverages large language models to generate image descriptions, reducing supervision needs and improving cross-modal retrieval performance.

Contribution

Proposes PiTL, a new approach using LLMs to generate image descriptions from category labels, creating a large VL dataset with minimal supervision.

Findings

PiTL improves cross-modal retrieval accuracy over existing W-VLP methods.

The created IN14K dataset contains 9 million images and 1 million descriptions.

VL models trained with PiTL data outperform others in image-to-text and text-to-image tasks.

Abstract

Vision-language (VL) Pre-training (VLP) has shown to well generalize VL models over a wide range of VL downstream tasks, especially for cross-modal retrieval. However, it hinges on a huge amount of image-text pairs, which requires tedious and costly curation. On the contrary, weakly-supervised VLP (W-VLP) explores means with object tags generated by a pre-trained object detector (OD) from images. Yet, they still require paired information, i.e. images and object-level annotations, as supervision to train an OD. To further reduce the amount of supervision, we propose Prompts-in-The-Loop (PiTL) that prompts knowledge from large language models (LLMs) to describe images. Concretely, given a category label of an image, e.g. refinery, the knowledge, e.g. a refinery could be seen with large storage tanks, pipework, and ..., extracted by LLMs is used as the language counterpart. The knowledge supplements, e.g. the common relations among entities most likely appearing in a scene. We create IN14K, a new VL dataset of 9M images and 1M descriptions of 14K categories from ImageNet21K with PiTL. Empirically, the VL models pre-trained with PiTL-generated pairs are strongly favored over other W-VLP works on image-to-text (I2T) and text-to-image (T2I) retrieval tasks, with less supervision. The results reveal the effectiveness of PiTL-generated pairs for VLP.