Summary of Diffava: Personalized Text-to-audio Generation with Visual Alignment, by Shentong Mo et al.
DiffAVA: Personalized Text-to-Audio Generation with Visual Alignment
by Shentong Mo, Jing Shi, Yapeng Tian
First submitted to arxiv on: 22 May 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM)
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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 This paper proposes a novel approach to text-to-audio generation, dubbed DiffAVA, which leverages latent diffusion models and incorporates visual alignment to generate high-quality audio. By fine-tuning lightweight modules with frozen modality-specific encoders, the authors enable personalized text-to-sound generation that synchronizes with video frames. The proposed method uses a multi-head attention transformer to aggregate temporal information from video features and a dual multi-modal residual network to fuse visual representations with text embeddings. Experimental results on the AudioCaps dataset demonstrate competitive performance in visual-aligned text-to-audio generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using words to create sounds that match what’s happening in videos. Right now, most methods for doing this are pretty good, but they don’t really think about how the sound should match what’s being shown on screen. The authors of this paper came up with a new way to do it called DiffAVA, which uses special computer programs to make sure the sound and video are in sync. They tested their method on some audio clips and found that it worked pretty well. |
Keywords
* Artificial intelligence * Alignment * Diffusion * Fine tuning * Multi head attention * Multi modal * Residual network * Transformer
