# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 5 Conclusions  
In this paper, we propose a globally guided confidence enhancement network (GGCEN) for image-text matching, which enhances the confidence level of meaningful local matching pairs from a global perspective to obtain more accurate image-text matching results. Furthermore, the introduction of GPO pre-training to obtain global features and a self-attention mechanism further strengthen the results. A better global feature is used to guide fine-grained local interactions; thus, we not only improve the performance on image-text retrieval tasks but we double the convergence speed. Extensive experiments conducted on MSCOCO and Flikr30K datasets show that the proposed model surpasses the current best methods for image-text matching. Compared to the attention-based approach of simply aggregating from pairwise similarities between regions and words, we show that a well-represented global feature and the introduction of a global perspective in the local matching process to enhance the meaningful local matching pairs can further improve the performance of image-text matching. However, our method relies heavily on the accuracy of region detection and text description. Inaccurate region detection or ambiguous text description can lead to unreliable matching, which may affect the overall performance of our method. To address these limitations, future work could focus on developing more robust and accurate region detection and text description methods. Finally, further investigation could be conducted to explore better ways of combining the confidence of matched region-word pairs with local semantic similarities to achieve more accurate relevance measurement in image-text matching.  
Figure 4: Qualitative results of the image-to-text retrieval on Flickr30K. For each image query, we show the top-5 ranked caption. The symbol \(\surd\) represents the same with ground-truth captions, while \(\times\) represents the mismatches.  
Figure 5: Qualitative results of the text-to-image retrieval on MSCOCO dataset. We present a ranking of the top-3 retrieved images arranged from left to right for each textual query. The accurate matches are indicated by green boxes, while inaccurate matches are identified with red boxes.  
**Author Contributions:** Conceptualization, X.D. and G.T.; methodology, X.D.; software, X.D.; validation, X.D.; formal analysis, X.D.; investigation, X.D. and G.T.; resources, X.D. and G.T.; data curation, X.D.; writing--original draft preparation, X.D.; writing--review and editing, X.D., G.T. and M.W.; visualization, X.D.; supervision, G.T. and M.W.; project administration, G.T. and M.W.; funding acquisition, G.T. and M.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Natural Science Foundation of Autonomous. Region under Grant 2021D01C118 and in part by the Autonomous Region High-Level Innovative Talent Project under Grant 042419006.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets involved in this study are available in publicly accessible repositories.  
**Acknowledgments:** The authors are sincerely grateful to the editor and anonymous reviewers for their insightful and constructive comments, which helped us improve this work greatly.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.