Papers with Multimodal embedding models

2 papers
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings (2026.acl-long)

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Challenge: Existing approaches to embed multimodal models face limitations such as suboptimal causal attention in VLMs and limited diversity in training objectives and data.
Approach: They propose a framework for transforming pre-trained VLMs into bidirectional multimodal embedding models.
Outcome: The proposed model improves performance across MMEB and ViDoRe-v2 benchmarks and exhibits strong scalability with both model size and training data on MMEF.
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data (2025.findings-acl)

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Challenge: Multimodal embedding models encode multimedia inputs into latent vector representations.
Approach: They propose to synthesize multimodal multilingual data using a multimodal large language model . they identify three criteria for high-quality synthetic multimodal data .
Outcome: The proposed model outperforms existing models on the MMEB Benchmark and the XTD benchmark.

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