# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 4 Experiments
### Comparisons with State-of-the-Art Methods  
To evaluate the efficacy of our proposed GGCEN, we conducted a comprehensive comparative evaluation against the latest state-of-the-art approaches on the widely-used Flickr30K and MSCOCO datasets. To ensure a fair comparison, we employed the same feature extraction backbone for all the methods and thoroughly benchmarked our proposed method against the others. Specifically, Faster R-CNN was used for images and Bi-GRU was used for text to ensure a fair comparison. Through rigorous experimentation and analysis, we were able to demonstrate the superior performance of GGCEN over the existing state-of-the-art methods, thereby highlighting its potential as a powerful tool for solving challenging image-text matching tasks. Tables 1-3 present the comparison results for Flickr30K, MSCOCO 1K, and 5K, respectively. The experimental findings indicate that the proposed Globally Guided Confidence Enhancement Network (GGCEN) performs remarkably well on both the Flickr30K and MSCOCO datasets. Table 1 shows the experimental results of the GGCEN model compared with existing advanced methods on the Flickr30k dataset, where the model outperforms the current baseline and achieves the best performance. Compared with the VSRN model [10], which uses a typical global reasoning mechanism for image-text matching, the GGCEN model emphasizes the inference of fine-grained region-word relationships guided by well-represented global features. Meaningful matching pairs in a global view are enhanced in confidence and play a greater role in the image-text matching process, resulting in more accurate similarity. The results demonstrate that compared to the current state-of-the-art method CMCAN, the proposed GGCEN model improves the R@1 results for text retrieval and image retrieval by 0.8% and 1.3%, respectively, due to the introduction of the GPO [23] pooling method for better global feature representation. We do not rely on complex prior knowledge, but instead, use a way to enhance the confidence of meaningful matching pairs in a global view to achieving better results. The SGRAF model [20] also calculates the correspondence between global and local features and performs graph reasoning and attention filtering on similarity. Compared with the SGRAF model, the proposed GGCEN model achieves a 2.6% and 2.4% improvement in R@1 results for text and image retrieval, respectively. The GGCEN model enhances the confidence of meaningful region-word matching pairs during  
relationship reasoning, more accurately eliminating redundant information, and further enhancing matching performance. Furthermore, the model not only uses the relationship between fine-grained local alignment features but also introduces a global feature representation with advanced semantic information to focus on more abundant potential semantic information, and more effectively uses local and global features to mutually guide accurate matching. The above experimental results show that the proposed method fully and efficiently utilizes cross-modal interaction information and the relationships between them, more accurately aligning significant fine-grained features with global features, further reducing the distance between query samples and correct results, and achieving relatively significant improvements in R@1. More potential semantic information that can be aligned is explored in local reasoning, achieving good results in R@5 and R@10. As shown in Table 2, compared to the current best methods, the GGCEN model further improves text and image retrieval performance on the MSCOCO 1K dataset. Compared to the SGRAF model [20], our method improves R@1 results by 0.9% and 2.3% in text and image retrieval, respectively. The SGRAF model takes spatial information into account for text features and significantly improves the effectiveness of text retrieval, especially in R@1. However, the GGCEN model focuses on obtaining crucial alignment information