VisionKG: Towards a unified vision knowledge graph

Computer Vision (CV) has recently achieved signi_cant im-provements, thanks to the evolution of deep learning. Along with ad-vanced architectures and optimisations of deep neural networks, CV data for (cross-datasets) training, validating, and testing contributes greatly to the performance of CV models. Many CV datasets have been created for different tasks, but they are available in heterogeneous data formats and semantic representations. Therefore, it is challenging when one needs to combine different datasets either for training or testing purposes. This paper proposes a unified framework using the Semantic Web technology that provides a novel way to interlink and integrate labelled data across different data sources. We demonstrate its advantages via various sce-narios with the system framework accessible both online and via APIs.4.