Image interpretation and generation method integrating block sentinels and AAM in intelligent art design

In intelligent art and design, image interpretation generation plays a pivotal role in enabling designers to explore and implement creativity in accordance with detailed image descriptions. To achieve more significant results in image interpretation generation, this study innovatively transforms the image interpretation generation problem into a sequence-to-sequence problem. The proposed model is an enhancement of the attention mechanism-based encoding and decoding image interpretation generation model. It is achieved by integrating the block sentinel mechanism and the adaptive attention mechanism. The results showed that the proposed model achieved scores of 19.48 %, 132.52 %, 40.74 %, and 13.47 % in Meteor, Cider, Rouge_L, and Bleu4, which were significantly better than the other comparative models. Meanwhile, the running time of the model in simple and complex scenarios was only 0.38 s and 0.45 s, while the running time of the Up-Down model reached 1.74 s and 3.28 s, significantly higher than the research model. This finding suggests that the image interpretation generation model based on block sentinels and an adaptive attention mechanism can achieve satisfactory image interpretation generation results in various scenarios. The model has been shown to generate image interpretations that are both smoother and more coherent, and it has been demonstrated to possess a higher operational efficiency. This suggests that the model can serve as an effective image interpretation tool for the field of intelligent art and design.