What Really Works for Universal Multimodal Embeddings? A Controlled Study of Contrastive Training Strategies
Contrastive learning has become the standard paradigm for multimodal representation learning, aligning semantically matched pairs while separating mismatched ones in a shared embedding space. Although various contrastive training strategies have been proposed across negative selection, batch construction, and gradient modulation, their actual effectiveness under controlled, reproducible conditions remains poorly understood. This motivates a fundamental question: Do sophisticated contrastive strategies consistently bring meaningful downstream gains in multimodal embedding learning? To address this, we systematically evaluate representative strategies under a unified benchmarking framework ...
| Authors | Duo Li, Haojun Pan, Zuhao Yang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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