Text-to-image (T2I) generation has emerged as a crucial component of AI content generation, enabling the automatic creation of visual content from natural language descriptions. Within this rapidly evolving domain, evaluating whether a generated image and the associated text prompt are semantically aligned remains a significant challenge. The existing methods typically adopt implicit and holistic representations derived from pre-trained vision-language models, which fails to adequately explore semantic entities and their relationships in both visual and textual modalities. Motivated by the above facts, this paper presents Graph-Align, a novel graph-based T2I alignment evaluation model, which transforms T2I alignment into a graph matching problem. Specifically, a generated image and the corresponding text prompt are first represented as graph structures comprising nodes (entities) and edges (relations). Based upon the graph representations, two modules are designed to achieve fine-grained graph matching: Graph Fusion (GF) and Subgraph Search (SS). GF establishes latent semantic connections between visual and textual graphs. Then, SS searches the visual graph space to identify prompt-relevant subgraph regions. Finally, a T2I alignment score is obtained by calculating the similarity between the identified visual subgraphs and textual graphs. Extensive experiments and comparisons on three benchmark T2I alignment databases demonstrate the advantage of the proposed model. Codes are available at https://github.com/yzc-ippl/GraphAlign.
原文链接
分享
引用
