ModalityDependent CrossModal Retrieval Based on Graph Regularization Nowadays, the heterogeneity gap of different modalities is the key problem for crossmodal retrieval. In order to overcome heterogeneity gaps, potential correlations of different modalities need to be mined. At the same time, the semantic information of class labels is used to reduce the semantic gaps between different modalities data and realize the interdependence and interoperability of heterogeneous data. In order to fully exploit the potential correlation of different modalities, we propose a crossmodal retrieval framework based on graph regularization and modality dependence (GRMD). Firstly, considering the potential feature correlation and semantic correlation, different projection matrices are learned for different retrieval tasks, such as image query text (I2T) or text query image (T2I). Secondly, utilizing the internal structure of original feature space constructs an adjacent graph with semantic information constraints which can make different labels of heterogeneous data closer to the corresponding semantic information. The experimental results on three widely used datasets demonstrate the effectiveness of our method.

CrossMedia ImageText Retrieval Combined with Global Similarity and Local Similarity In this paper, we study the problem of imagetext matching in order to make the image and text have better semantic matching. In the previous work, people just simply used the pretraining network to extract image and text features and project directly into a common subspace, or change various loss functions on this basis, or use the attention mechanism to directly match the image region proposals and the text phrases. This is not a good match for the semantics of the image and the text. In this study, we propose a method of crossmedia retrieval based on global representation and local representation. We constructed a crossmedia twolevel network to explore better semantic matching between images and text, which contains subnets that handle both global and local features. Specifically, we not only use the selfattention network to obtain a macro representation of the global image but also use the local finegrained patch with the attention mechanism. Then, we use a twolevel alignment framework to promote each other to learn different representations of crossmedia retrieval. The innovation of this study lies in the use of more comprehensive features of image and text to design the two kinds of similarity and add them up in some way. Experimental results show that this method is effective in imagetext retrieval. Experimental results on the Flickr30K and MSCOCO datasets show that this model has a better recall rate than many of the current advanced crossmedia retrieval models.