Enhancing prompt-image alignment evaluations via cyclic mutual information maximization

摘要

Assessing AI-generated images remains a signi_x001C_cant chal-lenge because both visual quality and prompt-image alignment are vital.Existing metrics prioritize visual quality, but the exploration of semanticalignment with user prompts remains limited. To address this issue, wepropose a novel cyclic mutual information maximization framework forAI-generated image quality assessment (CMIM-AIGIQA). Unlike exist-ing methods, our framework focuses on promoting e_x001B_ective multimodalinformation fusion in the deep feature domain, which has cyclic mutualinformation maximization phases. In the forward phase, we maximize themutual information between text and image features to generate re_x001C_ned,cross-aware representations. The re_x001C_ned features are then integratedthrough a fusion network. In the backward phase, the mutual informationbetween the joint embedding and every single modality is maximized toensure that the joint embedding retains critical semantic and visual cues.Experiments on three datasets demonstrate that our method e_x001B_ectivelybridges the gap between visual quality and prompt-image alignment. Thecode is available at https://github.com/Buka-Xing/CMIM-AIGIQA.