Reproduce “DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Shentong Mo, Zehua Chen, Fan Bao, Jun Zhu
DiffGAP, developed by researchers at Tsinghua University and Shengshu AI, integrates a lightweight, bidirectional diffusion module into contrastive embedding spaces to enhance cross-modal understanding and generation. The approach achieved state-of-the-art results in video-to-audio generation, text-to-audio generation, and cross-modal retrieval, demonstrating significant improvements in performance while maintaining computational efficiency.
Key insights
- Integrating a bidirectional diffusion process directly within the contrastive embedding space significantly enhances cross-modal interaction and mitigates inherent noise.
- The Bidirectional Split Training (BST) strategy is crucial for balanced learning, enabling the model to effectively treat each modality as both a condition and a target.
- Operating diffusion on compact, one-dimensional embeddings results in a highly lightweight generative module (5.4MB parameters) that maintains strong performance with few sampling steps (e.g., 5 steps).
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.