Image-text bidirectional learning network based cross-modal retrieval

Hybrid DAER Based Cross-Modal Retrieval Exploiting Deep Representation Learning

Information retrieval across multiple modes has attracted much attention from academics and practitioners. One key challenge of cross-modal retrieval is to eliminate the heterogeneous gap between different patterns. Most of the existing methods tend to jointly construct a common subspace. However, very little attention has been given to the study of the importance of different fine-grained regions of various modalities. This lack of consideration significantly influences the utilization of the extracted information of multiple modalities. Therefore, this study proposes a novel text-image cross-modal retrieval approach that constructs a dual attention network and an enhanced relation network (DAER). More specifically, the dual attention network tends to precisely extract fine-grained weight information from text and images, while the enhanced relation network is used to expand the differences between different categories of data in order to improve the computational accuracy of similarity. The comprehensive experimental results on three widely-used major datasets (i.e., Wikipedia, Pascal Sentence, and XMediaNet) show that our proposed approach is effective and superior to existing cross-modal retrieval methods.

Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm

The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal samples, which causes a heterogeneity gap. To bridge the above-mentioned gap, we propose the deep adversarial learning triplet similarity preserving cross-modal retrieval algorithm to map different modal samples into the common space, allowing their feature representation to preserve both the original inter- and intra-modal semantic similarity relationship. During the training process, we employ GANs, which has advantages in modeling data distribution and learning discriminative representation, in order to learn different modal features. As a result, it can align different modal feature distributions. Generally, many cross-modal retrieval algorithms only preserve the inter-modal similarity relationship, which makes the nearest neighbor retrieval results vulnerable to noise. In contrast, we establish the triplet similarity preserving function to simultaneously preserve the inter- and intra-modal similarity relationship in the common space and in each modal space, respectively. Thus, the proposed algorithm has a strong robustness to noise. In each modal space, to ensure that the generated features have the same semantic information as the sample labels, we establish a linear classifier and require that the generated features’ classification results be consistent with the sample labels. We conducted cross-modal retrieval comparative experiments on two widely used benchmark datasets—Pascal Sentence and Wikipedia. For the image to text task, our proposed method improved the mAP values by 1% and 0.7% on the Pascal sentence and Wikipedia datasets, respectively. Correspondingly, the proposed method separately improved the mAP values of the text to image performance by 0.6% and 0.8% on the Pascal sentence and Wikipedia datasets, respectively. The experimental results show that the proposed algorithm is better than the other state-of-the-art methods.