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Global and Local Semantic Completion Learning for Vision-Language Pre-Training

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Proposed GLSCL task improves cross-modal alignment in multimodal datasets, highlighting new methodologies.

GLSCL task enhances both global-local alignment and local-local alignment in vision-language models.
Masked global semantic completion improves the representativeness of global features, boosting model performance.
New model achieves state-of-the-art results in vision-language benchmarks, such as image-text retrieval.
ALIGN-BENCH serves as a validation benchmark for assessing cross-modal alignment efficacy.

Tu et al. (2025) studied this question.

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