Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data (2401.08567v1)

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.

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

The paper presents a rigorous analysis and novel approach for learning cross-modal tasks using uni-modal data through a method called Connect, Collapse, Corrupt ($C^3$). Specifically, it addresses the challenge of leveraging a pre-trained multi-modal contrastive representation space to enable cross-modal tasks without requiring paired multi-modal datasets. The focus of the paper is the inherent geometric characteristics of the representation space, which have implications for the interchangeability of embeddings from different modalities, such as image, audio, video, and text.

The authors begin by acknowledging the abundance of uni-modal data and the scarcity of paired multi-modal data, underscoring the significance of a methodology that mitigates the latter's limitations. Multi-modal contrastive learning has shown promise in aligning representations from different modalities, though the space's geometry—particularly the modality gap between embeddings—is not well-understood. Through rigorous theoretical analysis, the authors illuminate the geometric landscape, proposing a modality gap comprised of a constant vector and alignment noise of Gaussian distribution which hinders interchangeable embedding use.

The three-step $C^3$ method proposed to bridge this modality gap aligns embeddings in a joint representation space for improved performance in cross-modal tasks:

1. Connect: Original embeddings from different modalities are connected through multi-modal contrastive learning. However, the inherent modality gap and alignment noise persist.
2. Collapse: To address the modality gap, the embedding mean of each modality is subtracted, harmonizing distributional differences and removing the most dominant disparity. This effectively closes the modality gap.
3. Corrupt: Noise is added to the embeddings during training, enhancing the model's robustness and performance by accounting for alignment noise. This step acts as a form of regularization, improving the network's ability to handle cross-modal tasks by making it less sensitive to slight variations in the embedding space.

The practicality and effectiveness of the proposed $C^3$ method are demonstrated through experiments on tasks such as zero-shot image, audio, and video captioning, as well as text-to-image generation. Results show that pre-trained encoders, when adapted through $C^3$, achieve state-of-the-art performance without reliance on paired multi-modal data. The superiority of the $C^3$ approach is largely due to its principled analysis and rectification of the representation space geometry, offering a cohesive solution for drawing upon abundant uni-modal data for cross-modal applications.

Beyond immediate applications, the implications for multi-modal learning are substantial. The $C^3$ method allows more efficient data utilization, fostering advancements in applications where collecting paired data is challenging or infeasible. Future developments may refine these methods, further optimizing the handling of uni-modal information to fuel advances across domains where multi-modal synthesis creates tangible benefits.

This work marks a significant step forward in how uni-modal data is leveraged for cross-modal tasks. As research progresses, it will be crucial to explore extensions and adaptations of this method to encompass broader applications and additional modalities, refining the underlying theory and practical execution of cross-modal learning. The harmonization of embedding spaces heralded by $C^3$ stands to shift the paradigm in multi-modal AI research, making a compelling case for the potential of uni-modal data-driven learning.