Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data

• URL: http://arxiv.org/abs/2401.08567v1
• Date: Tue, 16 Jan 2024 18:52:27 GMT
• Title: Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
• Authors: Yuhui Zhang, Elaine Sui, Serena Yeung-Levy
• Abstract summary: 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. We introduce a three-step method, $C3$ (Connect, Collapse, Corrupt), to bridge the modality gap, enhancing the interchangeability of embeddings.
• Score: 10.908771426089512
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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