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Unsupervised query correction can achieve superior performance by cleverly encoding phonetic and visual similarities, avoiding the pitfalls of intent drift.
Existing zero-shot multimodal information extraction models struggle with real-world scenarios containing both seen and unseen categories, but this work solves it by modeling hierarchical semantic relationships in hyperbolic space and aligning semantic similarity distributions.