Summary of Save: Segment Audio-visual Easy Way Using Segment Anything Model, by Khanh-binh Nguyen and Chae Jung Park
SAVE: Segment Audio-Visual Easy way using Segment Anything Model
by Khanh-Binh Nguyen, Chae Jung Park
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed SAVE model efficiently adapts the pre-trained segment anything model (SAM) to the Audio-Visual Segmentation (AVS) task, achieving effective audio-visual fusion and interaction during encoding. By incorporating image and audio encoder adapters into transformer blocks, SAVE reduces training and inference speed while outperforming other state-of-the-art methods on AVSBench datasets. The approach accelerates performance on real-world data, achieving 70-86% mIoU on various subsets. This study presents a lightweight solution for AVS, addressing the challenge of precisely identifying auditory elements within visual scenes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Audio-Visual Segmentation (AVS) tries to find specific sounds in videos. Researchers created a new way to do this called SAVE, which is faster and better than other methods. SAVE uses special tools to help computers understand both audio and video together. This makes it easier for computers to identify sounds in videos. The new approach works well on real-world data, making it more accurate than previous methods. |
Keywords
» Artificial intelligence » Encoder » Inference » Sam » Transformer
