Scene graph generation (SGG) methods extract relationships between objects. While most methods focus on improving top-down approaches, which build a scene graph based on detected objects from an off-the-shelf object detector, there is a limited amount of work on bottom-up approaches, which jointly detect objects and their relationships in a single stage.In this work, we present a novel bottom-up SGG approach by representing relationships using Composite Relationship Fields (CoRF). CoRF turns relationship detection into a dense regression and classification task, where each cell of the output feature map identifies surrounding objects and their relationships. Furthermore, we propose a refinement head that leverages Transformers for global scene reasoning, resulting in more meaningful relationship predictions. By combining both contributions, our method outperforms previous bottom-up methods on the Visual Genome dataset by 26% while preserving real-time performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.