Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal
  Data
Yuhui Zhang Elaine Sui Serena Yeung-Levy
Building cross-modal applications is challenging due to limited paired
multi-modal data. Recent works have shown that leveraging a pre-trained
multi-modal contrastive representation space enables cross-modal tasks to be
learned from uni-modal data. This is based on the assumption that contrastive
optimization makes embeddings from different modalities interchangeable.
However, this assumption is under-explored due to the poorly understood
geometry of the multi-modal contrastive space, where a modality gap exists. In
our study, we provide a theoretical explanation of this space's geometry and
introduce a three-step method, $C^3$ (Connect, Collapse, Corrupt), to bridge
the modality gap, enhancing the interchangeability of embeddings. Our $C^3$
method significantly improves cross-modal learning from uni-modal data,
achieving state-of-the-art results on zero-shot image / audio / video
captioning and text-to-image generation.