Self-Attention-Based Edge Computing Model for Synthesis Image to Text through Next-Generation AI Mechanism

نویسندگان

چکیده

Image synthesis based on natural language description has become a research hotspot in edge computing artificial intelligence. With the help of generative adversarial networks, field made great strides high-resolution image synthesis. However, there are still some defects authenticity synthetic single-target images. For example, will be abnormal situations such as “multiple heads” and mouths” when synthesizing bird graphics. Aiming at problems, text generation model SA-AttnGAN self-attention mechanism is proposed. (Attentional Generative Adversarial Network) refines features into word sentence to improve semantic alignment images; initialization stage AttnGAN, used stability text-generated model; multistage GAN network superimpose, finally Experimental data show that outperforms other comparable models terms Inception Score Frechet Distance; analysis shows this can learn background colour information correctly capture heads mouths. The structural components improved, AttnGAN generates incorrect images mouths.” Furthermore, successfully applied description-based clothing with good generalization ability.

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ژورنال

عنوان ژورنال: Mathematical Problems in Engineering

سال: 2022

ISSN: ['1026-7077', '1563-5147', '1024-123X']

DOI: https://doi.org/10.1155/2022/4973535