Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
원문 초록
In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition. The use of Neurosymbolic AI has been shown to achieve enhanced capabilities, including improved grounding, alignment, explainability, and reliability. However, due to its nascent stage, there is a lack of widely available real-world benchmark datasets tailored to Neurosymbolic AI tasks. To address this gap and support the evaluation of current and future methods, we introduce DSceneKG -- a suite of knowledge graphs of driving scenes built from real-world, high-quality scenes from multiple open autonomous driving datasets. In this article, we detail the construction process of DSceneKG and highlight its application in seven different tasks. DSceneKG is publicly accessible at: https://github.com/ruwantw/DSceneKG
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LifelongPR: Lifelong knowledge fusion for point cloud place recognition based on replay and prompt learning
$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting
MCAM: Multimodal Causal Analysis Model for Ego-Vehicle-Level Driving Video Understanding
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
Following the Clues: Experiments on Person Re-ID using Cross-Modal Intelligence
토론
이 논문과 연결된 포럼 글이 아직 없습니다.