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• Cross-modality (language and vision) adaptation is inherently more challenging than cross-domain adaptation.technical 80%
• Adding a bridge training stage as a modality adaptation learner can effectively align Large Language Model (LLM) parameters with vision tasks.method 90%
• Random label bridge training requires no manual labeling and helps LLM parameters adapt to vision foundation tasks.method 90%
• Partial bridge training is often advantageous for cross-modality adaptation.result 85%
• Certain layers in LLMs exhibit strong foundational properties that remain beneficial even without fine-tuning for visual tasks.technical 85%
• The ratio of outlier parameters in language pre-training models and vision pre-training models differs significantly.technical 80%
• Many prior studies have assumed that language pre-trained models are unsuitable for downstream visual tasks due to disparate parameter spaces.limitation 80%
• Partial bridge training opens up new avenues for leveraging language pre-trained parameters directly within vision models.result 75%

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