  Image-text matching aims to find matched cross-modal pairs accurately. While
current methods often rely on projecting cross-modal features into a common
embedding space, they frequently suffer from imbalanced feature representations
across different modalities, leading to unreliable retrieval results. To
address these limitations, we introduce a novel Feature Enhancement Module that
adaptively aggregates single-modal features for more balanced and robust
image-text retrieval. Additionally, we propose a new loss function that
overcomes the shortcomings of original triplet ranking loss, thereby
significantly improving retrieval performance. The proposed model has been
evaluated on two public datasets and achieves competitive retrieval performance
when compared with several state-of-the-art models. Implementation codes can be
found here.
