Clustering-based query representations with a novel multi-intent loss and a concordance rate metric improve healthcare search intent classification on two real-world log datasets.
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MLLMs ignore dial state geometry and cluster by appearance, causing inconsistency under variations; TriSCA's state-distance alignment, metadata supervision, and objective alignment improve robustness on clock and gauge benchmarks.
LBFTI decomposes faces into three layers with dedicated generators and a three-stage training process to invert templates into fine-grained, identity-preserving images, claiming 25.3% better TAR than prior methods.
citing papers explorer
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Enhancing Healthcare Search Intent Recognition with Query Representation Learning and Session Context
Clustering-based query representations with a novel multi-intent loss and a concordance rate metric improve healthcare search intent classification on two real-world log datasets.
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State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading
MLLMs ignore dial state geometry and cluster by appearance, causing inconsistency under variations; TriSCA's state-distance alignment, metadata supervision, and objective alignment improve robustness on clock and gauge benchmarks.
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LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction
LBFTI decomposes faces into three layers with dedicated generators and a three-stage training process to invert templates into fine-grained, identity-preserving images, claiming 25.3% better TAR than prior methods.