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UFFM achieves superior pseudo-label quality by harmonizing labeled and unlabeled data training, challenging the dominance of manual labeling in semi-supervised learning.
Sparse observations can enhance ocean modeling performance, challenging the reliance on complete datasets that limit model capabilities.
FlowPipe achieves a remarkable 11.96% accuracy improvement and 12.5x faster training convergence for data preparation pipelines by leveraging LLMs and advanced flow generation techniques.
Ocean4D achieves unprecedented stability and consistency in underwater 4D reconstruction, overcoming the limitations of traditional methods that fail to account for medium effects.
