Summary of Synchformer: Efficient Synchronization From Sparse Cues, by Vladimir Iashin et al.
Synchformer: Efficient Synchronization from Sparse Cues
by Vladimir Iashin, Weidi Xie, Esa Rahtu, Andrew Zisserman
First submitted to arxiv on: 29 Jan 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)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research paper presents an innovative approach to audio-visual synchronization, with a focus on “in-the-wild” videos like those found on YouTube. To overcome the challenges of sparse synchronization cues in these types of videos, the authors introduce a novel model that decouples feature extraction from synchronization modeling through multi-modal segment-level contrastive pre-training. This approach achieves state-of-the-art performance in both dense and sparse settings. The paper also extends the training data to AudioSet, a massive “in-the-wild” dataset, and explores new capabilities for synchronization models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to improve audio-visual synchronization in everyday videos like YouTube clips. The main idea is to create a better way to match what’s happening in an audio clip with what’s happening on the screen at the same time. To do this, the researchers developed a new model that learns to recognize patterns and relationships between sounds and visuals. This approach works really well even when there are few cues to help synchronize the audio and video. The paper also looks at how to use these models in real-world scenarios and makes them more understandable by showing what parts of the model are most important. |
Keywords
* Artificial intelligence * Feature extraction * Multi modal
