HyperAlign: Hyperbolic Entailment Cones for Adaptive Text-to-Image Alignment Assessment

ArXi:2601.04614v2 Announce Type: replace With the rapid development of text-to-image generation technology, accurately assessing the alignment between generated images and text prompts has become a critical challenge. Existing methods rely on Euclidean space metrics, neglecting the structured nature of semantic alignment, while lacking adaptive capabilities for different samples. To address these limitations, we propose HyperAlign, an adaptive text-to-image alignment assessment framework based on hyperbolic entailment geometry.