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• The proposed framework integrates a robust image generation and auto-labeling pipeline that creates a diverse and high-quality training dataset with material-centric images.method 90%
• The auto-labeling pipeline automatically assigns labels by fusing object semantics and material attributes in text prompts.method 90%
• The method includes a prior incorporation strategy to distill information from vision-language models (VLMs).method 90%
• The framework uses a joint fine-tuning method that optimizes a pre-trained vision foundation model alongside VLM-derived priors.technical 90%
• The synthetic dataset effectively captures the characteristics of real world materials.result 85%
• The integration of priors from vision-language models significantly enhances the final performance of material classification.result 85%
• Traditional material classification faces significant challenges due to the scarcity of annotated data.limitation 90%
• Existing solutions leveraging vision-language foundation models exhibit unsatisfying results in material recognition tasks.limitation 85%

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