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.

Attention After Attention: Reading Text in the Wild with Cross Attention Recent methods mostly regarded scene text recognition as a sequencetosequence problem. These methods roughly transform the image into a feature sequence and use the algorithms for sequencetosequence problem like CTC or attention to decode the characters. However, text in images is distributed in a twodimensional (2D) space and roughly converting the features of text into a feature sequence may introduce extra noise, especially if the text is irregular. In this paper, we propose a novel framework named cross attention network, which learns to attend to local features of a 2D feature map corresponding to individual characters. The network contains two 1D attention networks, which operates harmoniously in two directions. Thus, one of the attention modules vertically attends to the features corresponding to the whole text of 2D features and the other horizontal module selects the local features to decode individual characters. Extensive experiments are performed on various regular benchmarks, including SVT, ICDAR2003, ICDAR2013, and IIIT5KWords, which demonstrate that the proposed model either outperforms or is comparable to all previous methods. Moreover, the model is evaluated on irregular benchmarks including SVTPerspective, CUTE80 and ICDAR 2015. The performance on irregular benchmarks shows the robustness of our model.