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Shanghai Jiao Tong University
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Achieving 96.06% accuracy in laser welding penetration prediction with only 200 labeled images could revolutionize quality assurance in industrial applications.
Achieving over 80% accuracy in cross-process welding penetration prediction could revolutionize intelligent monitoring across diverse welding systems.
Embedding geometric intelligence into segmentation models can dramatically enhance the recovery of small vascular structures and improve overall topology fidelity.
WeldMamba achieves a remarkable 74.63% mIoU in predicting weld pool dynamics, setting a new benchmark for real-time welding applications.
LLMs can dynamically optimize the training curriculum of multimodal retrieval models, leading to significant gains in retrieval accuracy by adapting to the model's evolving state.
