# Globally Guided Confidence Enhancement Network for Image-Text Matching
## 2 Related Work
Through multiple stages, the DAN focuses on specific regions within the image and specific words within the text, collecting necessary information from both modalities and estimating their similarity by attending to shared semantics between the image and text. However, due to the high degree of coupling involved in alignment aggregation, these approaches have only been able to achieve limited alignment between the two modalities. Notably, authors of ref. [12] proposed a stacked cross-attention mechanism that leverages image regions and words as contexts to explore fine-grained semantic alignment, by aggregation of resulting local alignments through average pooling or Log-Sum-Exp pooling. This has achieved impressive performance, providing valuable insights into the research of image-text matching. Inspired by this work [12], many researchers refs. [13; 14; 15; 17; 18; 19; 20; 21; 28] have sought to design plausible cross-modal alignment mechanisms based on the local inference framework for satisfying visual semantic interactions to facilitate similarity measurement. One of these works, Ref. [13], proposed an Iterative Matching with Recurrent Attention Memory (IMRAM) method, which captures the correspondence between images and text through multi-step alignment. During the alignment process, memory extraction units extract alignment knowledge from early steps to later interactions. The approach aims to achieve more precise and accurate region-word correspondence, which greatly improves the local similarity and significantly reduces the distance between images and texts for positive image-text pairs. A Dual-Path Recurrent Neural Network (DP-RNN) was introduced by the authors of Ref. [14] that symmetrically processes images and text using Recurrent Neural Networks (RNNs). Specifically, given an input image-text pair, the model's image region input sequence is re-ordered according to the position of the most relevant word in the text. The RNN is then used to extract advanced features from the re-ordered region inputs, including joint information of semantically related regions. The model then utilizes a multi-attention cross-matching mechanism to better match the correspondence between image regions and text words. In Ref. [15] a Relation-wise Dual Attention Network (RDAN) was proposed to encode both local correlations and global dependencies between regions and words. This approach not only allows for the exploration of fine-grained local similarities but also enables the exploration of potential visual-semantic relationships, providing rich supplemental information for inferring visual-semantic alignment and measuring image-text similarity. Recently, a learnable vector-based similarity representation was proposed Ref. [20] to more effectively model cross-modal correlations, instead of using scalar cosine similarity to represent the alignment information, which is then used to enhance the interaction between global and local alignment via the utilization of graph convolutional networks. Based on graph convolutional networks [29], the authors of ref. [21] proposed a method of constructing multiple similarities and strengthening the alignment of regions and words. This construction method first divides the region features and word features into multiple blocks, and calculates the cosine similarity between each pair of blocks, making different blocks play different roles in graph transmission. However, the cosine similarity obtained by this block-wise calculation is not learnable, and the similarity representation depends on the block encoding of region features and word features. The local inference-based approaches have made further explorations in optimizing single modality-enhanced interaction, optimizing multimodal feature fusion or directly improving attention units. However, authors of these studies try to find the nearest region (word) features to the current word (region), which tends to capture the so-called \"associativity\".