MOMA: Mixture-of-Modality-Adaptations for Transferring Knowledge from Image Models Towards Efficient Audio-Visual Action Recognition
Bibliographic record
Abstract
In this work, we investigate how to transfer learned knowledge from pre-trained image models for the audio-visual domain without relying on a full finetuning paradigm. To achieve this objective, we propose a novel parameter-efficient scheme called Mixture-of-Modality-Adaptations (MoMA) for audio-visual action recognition, which consists of the dual-path spatial-temporal adaptation for visual modality, the acoustic-aware adaptation for audio modality, and the audio-visual multimodal adaptation for interacting different modalities. Through freezing the original parameters of pre-trained image backbones and introducing lightweight parameter-efficient adapters, our proposed MoMA efficiently adapts the image models to learn audio-visual representation without employing any audio-specific encoders and full finetuning. The experimental results on the action recognition benchmarks indicate that our MoMA achieves competitive or even better performance than existing methods while involving significantly fewer tunable parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".