A feature supervision approach using SigLIP 2 extracts multi-granularity vision-aligned text representations to supervise MM-DiT image branches, pushing the Pareto frontier for portrait generation across alignment, realism, and aesthetics.
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ERA models internal and external knowledge as independent Dirichlet belief masses and uses Dempster-Shafer Theory to quantify conflicts, enabling better abstention decisions in RAG systems.
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Pareto-Enhanced Portrait Generation: Vision-Aligned Text Supervision for Alignment, Realism, and Aesthetics
A feature supervision approach using SigLIP 2 extracts multi-granularity vision-aligned text representations to supervise MM-DiT image branches, pushing the Pareto frontier for portrait generation across alignment, realism, and aesthetics.
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ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
ERA models internal and external knowledge as independent Dirichlet belief masses and uses Dempster-Shafer Theory to quantify conflicts, enabling better abstention decisions in RAG systems.