LAVViT: Latent Audio-Visual Vision Transformers for Speaker Verification
Bibliographic record
Abstract
Recently, Vision Transformers (ViTs) have shown remarkable success in various computer vision applications. In this work, we have explored the potential of ViTs, pre-trained on visual data, for audio-visual speaker verification. To cope with the challenges of large-scale training, we introduce the Latent Audio-Visual Vision Transformer (LAVViT) adapters, where we exploit the existing pre-trained models on visual data without fine-tuning their parameters and train only the parameters of LAVViT adapters. The LAVViT adapters are injected into every layer of the ViT architecture to effectively fuse the audio and visual modalities using a small set of latent tokens, forming an attention bottleneck, thereby reducing the quadratic computational cost of cross-attention across the modalities. The proposed approach has been evaluated on the Voxceleb1 dataset and shows promising performance using only a few trainable parameters. Code is available at https://github.com/praveena2j/LAVViT
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".