Abstract: Understanding a visual scene can not only identify a single object in isolation,but also get the interaction between different objects.Generating scene graph can obtain all the tuples(subject-predicate-object) and describe the object relationships inside an image,which is widely used in image understanding tasks.To solve the problem that the existing scene graph generation models use complicated structures with slow inference speed,a scene graph generation model combining attention mechanism and feature fusion with Factorizable Net structure was proposed.Firstly,a image is decomposed into subgraphs,where each subgraph contains several objects and their relationships.Then,the position and shape information is merged in the object features,and the attention mechanism is used to realize the message transmission between the object features and the subgraph features.Finally,the object classification and the relationship between the objects are inferred according to the object features and the subgraph features.The experimental results show that the accuracy of the visual relationship detection is 22.78% to 25.41%,and the accuracy of the scene graph generation is 16.39% to 22.75%,which is 1.2% and 1.8% higher than Factorizable Net on multiplevi-sual relationship detection datasets.Besides,the proposed model can perform object relationship detection task in 0.6 seconds with a GTX 1080Ti graphics.The results demonstrate that the number of image regions to be inferred is significantly reduced by using the subgraph structure.The feature fusion method and the attention mechanism are used to improve the performance of depth features,so the objects and their relationships can be predicted more quickly and accurately.Therefore,it solves the problem of poor timeliness and low accuracy in the traditional scene graph generation models.

Key words: Scene graph, Visual relationship detection, Attention mechanism, Message transmission, Feature fusion