Summary of Mmdisco: Multi-modal Discriminator-guided Cooperative Diffusion For Joint Audio and Video Generation, by Akio Hayakawa et al.
MMDisCo: Multi-Modal Discriminator-Guided Cooperative Diffusion for Joint Audio and Video Generation
by Akio Hayakawa, Masato Ishii, Takashi Shibuya, Yuki Mitsufuji
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel method to construct an audio-video generative model with minimal computational cost, leveraging pre-trained single-modal generative models for audio and video. It guides these base models to cooperatively generate well-aligned samples across modalities using a lightweight joint guidance module. This module adjusts scores separately estimated by the base models to match the score of the joint distribution over audio and video. The paper demonstrates that this guidance can be computed using the gradient of the optimal discriminator, which distinguishes real audio-video pairs from fake ones independently generated by the base models. The method also adopts a loss function to stabilize the discriminator’s gradient and make it work as a noise estimator, as in standard diffusion models. Empirically, the paper shows that this approach improves both single-modal fidelity and multimodal alignment with relatively few parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates an audio-video generative model that doesn’t use too much computer power. It uses pre-trained models for audio and video to make new samples that are well-matched between the two modalities. The model adjusts its scores based on how well the audio and video match each other, which helps create realistic fake data. This approach also helps improve the noise in the generated data by using a special loss function. Overall, this method is good at making fake audio-video pairs that look like real ones. |
Keywords
» Artificial intelligence » Alignment » Generative model » Loss function
