Formalizes incompleteness divergence across missing-data protocols in IMVC and proposes CRAFT, a mask-aware transformer enabling train-once robustness to diverse missing patterns.
Unified embedding alignment with missing views inferring for incomplete multi-view clustering
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Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC
Formalizes incompleteness divergence across missing-data protocols in IMVC and proposes CRAFT, a mask-aware transformer enabling train-once robustness to diverse missing patterns.