Summary of Unsupervised Audio-visual Segmentation with Modality Alignment, by Swapnil Bhosale et al.
Unsupervised Audio-Visual Segmentation with Modality Alignment
by Swapnil Bhosale, Haosen Yang, Diptesh Kanojia, Jiangkang Deng, Xiatian Zhu
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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 paper introduces unsupervised Audio-Visual Segmentation (AVS) that identifies objects at the pixel level based on sounds, eliminating the need for expensive annotations. The authors propose Modality Correspondence Alignment (MoCA), a method that combines foundation models like DINO, SAM, and ImageBind to associate visual and audio signals. MoCA leverages their knowledge complementarity and optimizes joint usage for multi-modality association. The approach involves estimating positive and negative image pairs in the feature space, introducing an audio-visual adapter, and using a novel pixel matching aggregation strategy within an image-level contrastive learning framework. Experimental results on AVSBench and AVSS datasets demonstrate that MoCA outperforms baseline methods and approaches supervised counterparts, particularly in complex scenarios with multiple auditory objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding the world by linking what we see to what we hear. Right now, it’s hard to teach computers this skill because it requires a lot of training data. The authors came up with a new way to do this without needing so much data. They combined different computer models that are good at understanding images and sounds to make a new model that can link them together. This new model is called Modality Correspondence Alignment (MoCA). The authors tested their model on some big datasets and found that it works really well, especially when there are multiple things making sounds in the same scene. |
Keywords
» Artificial intelligence » Alignment » Sam » Supervised » Unsupervised
