UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks

TL;DR

This paper introduces UniBoost, a method that uses large-scale unsupervised unimodal pre-training to significantly improve zero-shot vision-language task performance, surpassing existing multimodal models like CLIP.

Contribution

The paper demonstrates that unimodal pre-training enhances zero-shot vision-language understanding, offering broader data coverage and reducing misalignment issues compared to joint multimodal training.

Findings

Unimodal pre-training outperforms CLIP-based models by 6.5% on PASCAL-5i.

Unimodal pre-training improves COCO-20i segmentation by 6.2%.

Models learn richer representations of images and text, boosting zero-shot performance.

Abstract

Large-scale joint training of multimodal models, e.g., CLIP, have demonstrated great performance in many vision-language tasks. However, image-text pairs for pre-training are restricted to the intersection of images and texts, limiting their ability to cover a large distribution of real-world data, where noise can also be introduced as misaligned pairs during pre-processing. Conversely, unimodal models trained on text or image data alone through unsupervised techniques can achieve broader coverage of diverse real-world data and are not constrained by the requirement of simultaneous presence of image and text. In this paper, we demonstrate that using large-scale unsupervised unimodal models as pre-training can enhance the zero-shot performance of image-text pair models. Our thorough studies validate that models pre-trained as such can learn rich representations of both modalities, improving their ability to understand how images and text relate to each other. Our experiments show that unimodal pre-training outperforms state-of-the-art CLIP-based models by 6.5% (52.3% $\rightarrow$ 58.8%) on PASCAL-5$^i$ and 6.2% (27.2% $\rightarrow$ 33.4%) on COCO-20$^i$ semantic segmentation under zero-shot setting respectively. By learning representations of both modalities, unimodal pre-training offers broader coverage, reduced misalignment errors, and the ability to capture more complex features and patterns in the real-world data resulting in better performance especially for zero-shot vision-language tasks.