# Image-Caption Model Based on Fusion Feature
## 1 Introduction  
With the rapid development of multimedia technology and computer networks, the multi-modal character of data has become more prominent, and the amount of data has exploded. There is a growing demand for multi-modal data processing in academia and business circles.  
[PERSON] et al. [1] proposed the task of image captioning. Their task was to realize modal conversion from the image mode to the text mode through the model, which was embodied as a binary group (I, S). The model completed the modal conversion from image mode I (Image) to text mode S (Sentence). Image captioning is a cross-task between Computer Vision (CV) and Natural Language Processing (NLP). This task is straightforward for humans with particular life experiences. However, it is a considerable challenge for computers, which not only requires computers to understand the content of images but also requires computers to generate sentences with human language habits. Image captioning is essential in image understanding, human-computer interaction, assisting visually impaired people, and journalism. Because of its importance and challenge, it is becoming a hotspot in artificial intelligence.  
In the early stages of task studies, most researchers used machine-learning methods that required much human effort to annotate, extract manually, and evaluate datasets, resulting in slow progress, poor model performance, and poor caption quality. With the rise of deep learning, image captioning, for which the tasks have significantly progressed, has become a mainstream method. This method employs CNN as the encoder to extract image features. Then the model sends them to Long Short-Term Memory Network (LSTM), which decodes features to caption. This approach avoids wasting resources and produces more flexible and high-quality captions. Recently, GCN has been used as an encoder inmany works to improve the model's performance. The difference between CNN and GCN is that CNN extracts the grid-level features of the image. Grid-level features contain contextual and fine-grained information about the image, but some semantic information, such as the interrelationships and location relationships between multiple objects, is not included. GCN extracts the regional features of the image and relies on target detection techniques to accurately capture semantic information such as targets, target attributes, and the interrelationship between targets. However, it is object-oriented and ignores the fine-grained and contextual information of the rest of the image.  
In order to solve this problem, this paper proposes an image caption-model based on the fusion feature. This model employs a fused feature approach, where an attention mechanism dynamically captures grid-level features and region-level features related to captioning, and they are weighted and fused. The fusion features are more capable of capturing the semantic relationships between objects and retain a certain degree of fine-grained and contextual information. Our captions are richer in information than traditional captions and can more fully express the relationships between objects in the images. The main contributions of this paper are as follows:  
1. The grid-level features of an image are extracted by a Res-Net101 with squeeze and excitation operations. The GCN extracts the region-level features. The feature-fusion module weights fusion of the two features of the joint attention mechanism to learn image features and semantic information better.
2. Fusion-feature vectors are processed by a two-layer LSTM, which adds an attention mechanism to obtain the context information of the heading and dramatically improves the quality of the heading.
3. The algorithm was trained and tested, and the experimental results show that the algorithm achieved excellent performance.  
The remaining part of the article is structured as follows. Section 2 describes related work, Section 3 details the model and method, Section 4 presents the experimental results, and Section 5 concludes the paper.