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✨FORGE:Fine-grained Multimodal Evaluation for Manufacturing Scenarios
📝 Summary:
FORGE introduces a multimodal manufacturing dataset, revealing that MLLM performance is limited by domain-specific knowledge, not visual grounding. Fine-tuning on FORGEs annotations significantly improves accuracy, offering a path for domain-adapted MLLMs.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07413
• PDF: https://arxiv.org/pdf/2604.07413
• Project Page: https://ai4manufacturing.github.io/forge-web/
• Github: https://github.com/AI4Manufacturing/FORGE
✨ Datasets citing this paper:
• https://huggingface.co/datasets/AI4Manufacturing/forge
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#FORGE #MLLM #ManufacturingAI #MultimodalAI #DomainAdaptation
📝 Summary:
FORGE introduces a multimodal manufacturing dataset, revealing that MLLM performance is limited by domain-specific knowledge, not visual grounding. Fine-tuning on FORGEs annotations significantly improves accuracy, offering a path for domain-adapted MLLMs.
🔹 Publication Date: Published on Apr 8
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.07413
• PDF: https://arxiv.org/pdf/2604.07413
• Project Page: https://ai4manufacturing.github.io/forge-web/
• Github: https://github.com/AI4Manufacturing/FORGE
✨ Datasets citing this paper:
• https://huggingface.co/datasets/AI4Manufacturing/forge
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#FORGE #MLLM #ManufacturingAI #MultimodalAI #DomainAdaptation