Objectbased Image Discrimination Relationship Recognition With the development of selfmedia, we can publish images freely on the Internet, and there will be some discriminatory images more or less. Discrimination is an emotional relationship. However, there is no open data set to help us complete the task of discriminatory image detection. In this article, we created a simple data set that can be used to detect discriminatory relationships. Then, on this data set, we propose a baseline for discriminatory relationship detection. This baseline is a multitask network based on translation embedding model, which can output both the action categories of objects in the graph and the relationship categories between the two. Experiments show that our multitask network can effectively detect relationships, and it is better than the simple translation embedding model.

Characterization and classification of semantic imagetext relations The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics CrossModal Mutual Information and Semantic Correlation in order to model complex imagetext relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex imagetext relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic imagetext classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight imagetext classes. Experimental results show the feasibility of the approach, whereby the predictall approach outperforms the cascaded approach of the metric classifiers.