Unifying Vision-Language Representation Space with Single-tower Transformer
This work addresses the challenge of multi-modal representation learning for vision-language tasks, offering a novel approach that could impact AI applications in image-text understanding.
The paper tackles the problem of learning a unified vision-language representation space by treating images and captions as different views of mutual information, resulting in a modality-agnostic framework called OneR that enables tasks like zero-shot object localization and multi-modal retrieval.
Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a generic one-tower model for vision-language pretraining (VLP), and propose OneR as a simple yet effective framework for our goal. We discover intriguing properties that distinguish OneR from the previous works that learn modality-specific representation spaces such as zero-shot object localization, text-guided visual reasoning and multi-modal retrieval, and present analyses to provide insights into this new form of multi-modal representation learning. Thorough evaluations demonstrate the potential of a unified modality-agnostic VLP framework.