# Image-Caption Model Based on Fusion Feature
## 4 Results
### Experimental Results and Analysis  
Firstly, the validity of the model is verified. In this paper, five state-of-the-art-level image-captioning models in the last three years are selected for comparison, and the results are shown in Table 1. the scoring results are all on the MS COCO dataset with the condition of five reference captions. (1) High-Level Attention is the image caption generation model proposed by [PERSON] et al. [20]. The model adopts a CNN-LSTM architecture with an embedded bottom-up attention mechanism. The model can score low-level features (contrast, sharpness, and clarity) and high-level features (face impact) of image regions and combine the scores to determine the regions to which attention should be directed. (2) CGVRG is an image-caption-generation model based on a caption-guided visual relationship graph (CGVPR) proposed by [PERSON] et al. [15]. The model uses the features and context vectors of the graph structure to guide the decoder to generate captions so that the model has better semantic information. (3) M\({}^{2}\)-Transformer is a model proposed by [PERSON] et al. [21], adopting the transformer architecture. The framework learns the multilevel representation of the relationship between image regions. It uses grid-like connections to exploit the properties of the lower and higher levels in the decoding phase, again reducing the complexity of the model and mitigating the semantic gap. (4) RDN (Reflective Decoding Network) is a model proposed by [PERSON] et al. [22]. The CNN-LSTM architecture is embedded in the RDN. The framework enhances the decoder's long-sequence dependence and improves the decoder's long-sequence modeling capability. (5) Entangle-Transformer is a model proposed by [PERSON] et al. [23]. The transformer [24,25] model is also adopted. The framework alleviates the semantic gap. (6) DCLT is a Transformer-based model proposed by [PERSON] et al. [26] that uses two Transformers to extract two levels of features and fuse them. Compared with the above five models, the results of the five evaluation metrics consistently show that our work is favorable with other state-of-the-art techniques, which include CNN-based (High-Level Attention and RDN), GCN-based (CGVRG), and Transformer-based models. Our model's CIDEr score and BLEU-4 score can reach 131% and 39.9%, which are 2.4% and 0.5% better than the best comparison models, respectively. Furthermore, inspired by the fact that all five better models contain attention mechanisms, the model adds a fusion-feature attention mechanism at a later stage to improve the performance. From the analysis of the model architecture and its performance, the current image-caption-generation models, mainly GCN and Transformer, have gradually replaced the mainstreamencoder position of CNN because of their excellent performance. We performed a second set of ablation experiments to further explore the model's advantages over the traditional CNN and GCN, as shown in Table 2.  
In order to investigate the superiority of this model over the traditional model, several ablation experiments were carried out. First, a CNN-LSTM architecture with no embedded attention mechanism was used as the baseline results. The first set of ablation experiments in this study was CNN-LSTM architecture based on an embedded MLP attention mechanism. The results showed that the ablation model based on the embedded attention mechanism has good scoring performance. GCN-LSTM was the framework for the second set of experiments. The data show that GCN has a significant performance advantage over CNN, and CNN-LSTM-Attention, with its CIDEr score of 39.1% and 33.3% higher than CNN-LSTM and CNN-LSTM-Attention, respectively. Due to the excellent performance of the attention mechanism, we embedded the attention mechanism in the GCN-LSTM architecture, and its performance in the CIDE metric increased by another 2.6%, further validating the attention mechanism in the image-caption-generation model.