Summary of Dynamic Cross Attention For Audio-visual Person Verification, by R. Gnana Praveen et al.
Dynamic Cross Attention for Audio-Visual Person Verification
by R. Gnana Praveen, Jahangir Alam
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 This paper proposes a novel approach to person or identity verification, leveraging audio-visual fusion to outperform unimodal approaches. The authors introduce Dynamic Cross-Attention (DCA), a model that dynamically selects features based on the strength of complementary relationships between audio and visual modalities. A conditional gating layer evaluates the contribution of cross-attention and chooses attended features only when they exhibit strong relationships, using the Voxceleb1 dataset for evaluation. The proposed model consistently improves performance on multiple variants of cross-attention and outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research aims to improve how we verify identities by combining audio and visual information. The team developed a new way to choose which features from each modality are most useful based on how well they work together. This approach was tested using a large dataset of voices and faces and showed better results than previous methods. |
Keywords
* Artificial intelligence * Cross attention
