MAC: Multimodal Attentive Contrastive Learning Framework
Bibliographic record
Abstract
The growing capabilities of large-scale models in text and audio have significantly advanced multimodal learning. However, many downstream tasks still suffer from insufficient labeled data, challenging the effective learning of robust multimodal representations. To address these challenges, we propose the Multimodal Attentive Contrastive (MAC) learning framework, which integrates contrastive learning with a Mixture of Experts (MoE) mechanism to enhance multimodal representation learning for text and audio data. Our approach leverages pre-trained foundation models to generate high-quality unimodal embeddings, which are further refined through unsupervised contrastive learning. This contrastive model aligns multimodal audio-text pairs, improving their joint representation. A novel MoE-based attention mechanism is introduced, wherein modality-specialized expert networks dynamically combine these embeddings based on sample-specific gating weights. This design enhances the model's ability to balance modality contributions, especially in low-data settings. We perform extensive empirical evaluations on multiple pre-trained language and audio models, comparing different contrastive training configurations and validating the effectiveness of MAC through rigorous cross-validation experiments. Empirical results demonstrate that our framework improves downstream classification performance by effectively leveraging contrastive objectives and MoE, outperforming traditional multimodal learning approaches, particularly in low-data scenarios.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".