GeneAnnotator: A Semi-automatic Annotation Tool for Visual Scene Graph
This tool addresses the annotation bottleneck for scene graph datasets in computer vision, enabling applications like image captioning and VQA, but it is incremental as it builds on existing annotation methods.
The authors introduced GeneAnnotator, a semi-automatic annotation tool for creating scene graphs from images, and used it to build Traffic Genome, a dataset with 1000 traffic images, demonstrating its effectiveness in reducing annotation workload.
In this manuscript, we introduce a semi-automatic scene graph annotation tool for images, the GeneAnnotator. This software allows human annotators to describe the existing relationships between participators in the visual scene in the form of directed graphs, hence enabling the learning and reasoning on visual relationships, e.g., image captioning, VQA and scene graph generation, etc. The annotations for certain image datasets could either be merged in a single VG150 data-format file to support most existing models for scene graph learning or transformed into a separated annotation file for each single image to build customized datasets. Moreover, GeneAnnotator provides a rule-based relationship recommending algorithm to reduce the heavy annotation workload. With GeneAnnotator, we propose Traffic Genome, a comprehensive scene graph dataset with 1000 diverse traffic images, which in return validates the effectiveness of the proposed software for scene graph annotation. The project source code, with usage examples and sample data is available at https://github.com/Milomilo0320/A-Semi-automatic-Annotation-Software-for-Scene-Graph, under the Apache open-source license.