🤖 AI Summary
Current text-to-image generation methods face significant bottlenecks in semantic alignment accuracy and structural consistency. To address these limitations, we propose a dual-path optimization framework integrating contrastive alignment and structural guidance. First, a cross-modal contrastive learning module is introduced to enhance fine-grained semantic alignment within the CLIP embedding space. Second, structural priors—including layout maps and edge sketches—are explicitly incorporated to enforce geometric consistency. The framework jointly optimizes contrastive loss, structural reconstruction loss, and adversarial loss, improving generation controllability and fidelity without increasing inference overhead. Extensive experiments on COCO-2014 demonstrate state-of-the-art performance: +3.2% CLIP Score, −12.7% FID, and +5.8% SSIM over prior methods. Generated images exhibit superior semantic correctness and geometric integrity.
📝 Abstract
This paper addresses the performance bottlenecks of existing text-driven image generation methods in terms of semantic alignment accuracy and structural consistency. A high-fidelity image generation method is proposed by integrating text-image contrastive constraints with structural guidance mechanisms. The approach introduces a contrastive learning module that builds strong cross-modal alignment constraints to improve semantic matching between text and image. At the same time, structural priors such as semantic layout maps or edge sketches are used to guide the generator in spatial-level structural modeling. This enhances the layout completeness and detail fidelity of the generated images. Within the overall framework, the model jointly optimizes contrastive loss, structural consistency loss, and semantic preservation loss. A multi-objective supervision mechanism is adopted to improve the semantic consistency and controllability of the generated content. Systematic experiments are conducted on the COCO-2014 dataset. Sensitivity analyses are performed on embedding dimensions, text length, and structural guidance strength. Quantitative metrics confirm the superior performance of the proposed method in terms of CLIP Score, FID, and SSIM. The results show that the method effectively bridges the gap between semantic alignment and structural fidelity without increasing computational complexity. It demonstrates a strong ability to generate semantically clear and structurally complete images, offering a viable technical path for joint text-image modeling and image generation.