Summary of Aligning Audio-visual Joint Representations with An Agentic Workflow, by Shentong Mo et al.
Aligning Audio-Visual Joint Representations with an Agentic Workflow
by Shentong Mo, Yibing Song
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 for improving audio-visual (AV) joint representations by aligning audio signals with visual data from a data-centric perspective. The authors introduce an LLM-based assistant, AVAgent, which uses multi-modal language models to convert audio and visual data into language descriptions. AVAgent then reasons about the alignment of the paired data and plans to edit the audio signal if necessary, executing predefined actions to filter noise or augment data. The paper demonstrates state-of-the-art performance in diverse downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to improve how audio and video work together. They created a system that helps align the sounds with what’s being shown on screen. This is done by using special language models that can understand both audio and video. The system, called AVAgent, makes sure the sound is in sync with the video and makes any necessary changes. The results show that this approach outperforms previous methods. |
Keywords
» Artificial intelligence » Alignment » Multi modal
