Papers by Yueze Wang

2 papers
MegaPairs: Massive Data Synthesis for Universal Multimodal Retrieval (2025.acl-long)

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Challenge: despite the growing demand for multimodal retrieval, there is a lack of training data.
Approach: They propose a data synthesis method that leverages vision language models and open-domain images to generate high-quality data.
Outcome: The proposed method outperforms baseline models on 70 more datasets and can scale up.
Universal Prompt Optimizer for Safe Text-to-Image Generation (2024.naacl-long)

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Challenge: Existing studies based on image checker, model fine-tuning and embedding blocking are impractical in real-world applications.
Approach: They propose a novel reward function measuring toxicity and text alignment of generated images and train the optimizer through Proximal Policy Optimization.
Outcome: The proposed model reduces the likelihood of various models in generating inappropriate images, with no significant impact on text alignment.

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