Summary of Equiav: Leveraging Equivariance For Audio-visual Contrastive Learning, by Jongsuk Kim et al.
EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning
by Jongsuk Kim, Hyeongkeun Lee, Kyeongha Rho, Junmo Kim, Joon Son Chung
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 In this research paper, the authors introduce EquiAV, a novel framework for audio-visual contrastive learning that leverages equivariance to address limitations in previous approaches. The proposed method begins by extending equivariance to audio-visual learning through a shared attention-based transformation predictor, which enables the aggregation of features from diverse augmentations into a representative embedding. This provides robust supervision with minimal computational overhead. The authors verify the effectiveness of EquiAV through extensive ablation studies and qualitative results, outperforming previous works across various audio-visual benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EquiAV is a new way to learn about sounds and videos together. The problem was that earlier methods didn’t work well when they changed or augmented the input data. To fix this, the researchers created a special tool that helps audio-visual learning by using something called equivariance. This means it can combine features from different versions of the same sound and video into one helpful representation. They tested their method and showed it works better than previous methods on various tasks. |
Keywords
* Artificial intelligence * Attention * Embedding
