Papers with compressed pre-training phase

1 papers
Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding (2026.acl-long)

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Challenge: Recent approaches demonstrate that MLLMs can be adapted into competitive embedding models via large-scale contrastive learning.
Approach: They propose a compressed pre-training phase which serves as a warm-up stage for contrastive learning.
Outcome: The proposed model achieves state-of-the-art among MLLMs of comparable size on the MMEB, realizing optimization in both efficiency and effectiveness.

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