This application presents a method for providing clear, accurate, and intelligent AI representations of complex, multi-element images. It employs an improved Faster R-CNN model as the
image processing model, replaces VGG-16 with DenseNet for
feature extraction, optimizes bounding box selection through dimensional clustering, and enhances model stability by predicting relative
grid cell coordinates. A
text generation architecture is constructed based on a hierarchical recursive network. The first layer, a global long short memory layer, determines the topic vector based on the fusion vector to increase the number of descriptive sentences. The second layer, a
sentence BRNN layer, generates accurate and rich descriptive sentences. A fully convolutional localization layer effectively integrates and transfers block features, constructing an end-to-end architecture that collaboratively optimizes
image processing and
text generation. This method addresses the problems of high
model complexity, inaccurate bounding box localization, and insufficient association of descriptive sentences in existing technologies, generating denser, more accurate, and natural
descriptive language that conforms to language habits. It is suitable for various application scenarios such as visual intelligent
question answering and video
subtitle generation.