Generating scene graph to describe all the relations inside an image gains increasing interests these years. However, most of the previous methods use complicated structures with slow inference speed or rely on the external data, which limits the usage of the model in real-life scenarios. To improve the efficiency of scene graph generation, we propose a subgraph-based connection graph to concisely represent the scene graph during the inference. A bottom-up clustering method is first used to factorize the entire scene graph into subgraphs, where each subgraph contains several objects and a subset of their relationships. By replacing the numerous relationship representations of the scene graph with fewer subgraph and object features, the computation in the intermediate stage is significantly reduced. In addition, spatial information is maintained by the subgraph features, which is leveraged by our proposed Spatial-weighted Message Passing (SMP) structure and Spatial-sensitive Relation Inference (SRI) module to facilitate the relationship recognition. On the recent Visual Relationship Detection and Visual Genome datasets, our method outperforms the state-of-the-art method in both accuracy and speed.
The most related works are the methods proposed by Xu al @cite_30 and Li al @cite_12 , both of which jointly detect the objects and recognize their relationships. In @cite_30 , the scene graph was constructed by refining the object and predicate features jointly in an iterative way. In @cite_12 , region caption was introduced as a higher-semantic-level task for scene graph generation, so the objects, pair-wise relationships and region captions help the model learn representations from three different semantic levels. Our method differs in two aspects: (1) We propose a more concise graph to represent the connections between objects instead of enumerating every possible pair, which significantly reduces the computation complexity and allows us to use more object proposals; (2) Our model could learn to leverage the spatial information embedded in the subgraph feature maps to boost the relationship recognition. Experiments show that the proposed framework performs substantially better and faster in all different task settings.
@cite_30 @cite_12
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Understanding a visual scene goes beyond recognizing individual objects in isolation. Relationships between objects also constitute rich semantic information about the scene. In this work, we explicitly model the objects and their relationships using scene graphs, a visually-grounded graphical structure of an image. We propose a novel end-to-end model that generates such structured scene representation from an input image. Our key insight is that the graph generation problem can be formulated as message passing between the primal node graph and its dual edge graph. Our joint inference model can take advantage of contextual cues to make better predictions on objects and their relationships. The experiments show that our model significantly outperforms previous methods on the Visual Genome dataset as well as support relation inference in NYU Depth V2 dataset. Object detection, scene graph generation and region captioning, which are three scene understanding tasks at different semantic levels, are tied together: scene graphs are generated on top of objects detected in an image with their pairwise relationship predicted, while region captioning gives a language description of the objects, their attributes, relations and other context information. In this work, to leverage the mutual connections across semantic levels, we propose a novel neural network model, termed as Multi-level Scene Description Network (denoted as MSDN), to solve the three vision tasks jointly in an end-to-end manner. Object, phrase, and caption regions are first aligned with a dynamic graph based on their spatial and semantic connections. Then a feature refining structure is used to pass messages across the three levels of semantic tasks through the graph. We benchmark the learned model on three tasks, and show the joint learning across three tasks with our proposed method can bring mutual improvements over previous models. Particularly, on the scene graph generation task, our proposed method outperforms the stateof- art method with more than 3 margin. Code has been made publicly available.