Spatial Relation

Spatial relation research focuses on understanding and modeling how objects relate to each other in space, aiming to improve machine perception and reasoning capabilities. Current research emphasizes the use of transformer-based models, graph neural networks, and large language models (LLMs) to capture complex spatial relationships, often integrating information from multiple modalities (e.g., vision and language). This work is crucial for advancing artificial intelligence in areas such as robotics, autonomous driving, and medical image analysis, where accurate spatial understanding is essential for effective decision-making. Significant efforts are also dedicated to developing robust benchmarks and datasets for evaluating spatial reasoning performance across various model architectures.