Semantic-aware Mapping for Text-to-Image Synthesis

• DOI: 10.52783/jisem.v10i2.3135
• Publication Date: 2025-03-15T13:27:21Z
• Authors: Khushboo Patel
• Keywords: Image Synthesis

This study explores the fast-progressing domain of Text-to-Image (T2I) synthesis, which aims to bridge gap between language and visual comprehension. The main emphasis is on crucial significance Generative Adversarial Networks (GANs), have transformed process image formation, with a specific impact conditional GANs. models enable controlled generation, their influence production high-quality images extensively analyzed. We propose novel method generating semantically aware embeddings from input text description learns better mapping generate output image. Moreover, paper examines datasets in T2I research investigates development approaches. Ultimately, highlights persistent difficulties assessing models, particular quality measurements. It emphasizes necessity for complete evaluation methods that take into account both realism semantic coherence. Experimental results demonstrate our approach yields considerable performance over existing approaches generation.