Image Scene Graph Generation (SGG) Benchmark

By Xiao Han, Jianwei Yang, Houdong Hu, Lei Zhang, Jianfeng Gao, Pengchuan Zhang. 2021

42 citations recorded by the source corpus.

There is a surge of interest in image scene graph generation (object, attribute and relationship detection) due to the need of building fine-grained image understanding models that go beyond object detection. Due to the lack of a good benchmark, the reported results of different scene graph generation models are not directly comparable, impeding the research progress. We have developed a much-needed scene graph generation benchmark based on the maskrcnn-benchmark and several popular models. This paper presents main features of our benchmark and a comprehensive ablation study of scene graph generation models using the Visual Genome and OpenImages Visual relationship detection datasets. Our codebase is made publicly available at https://github.com/microsoft/scene_graph_benchmark.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering (2022, background): “Second, object-level information is translated to object and attribute labels [1, 15].”
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning (2023, unknown): “[28] to predict object locations and their categorical labels such as “knife” , “plate” and “napkin” .”