Enhancing prompt-image alignment evaluations via cyclic mutual information maximization

Assessing AI-generated images remains a signix001Ccant 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 ex001Bective 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 rex001Cned,cross-aware representations. The rex001Cned 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 ex001Bectivelybridges the gap between visual quality and prompt-image alignment. Thecode is available at https://github.com/Buka-Xing/CMIM-AIGIQA.