Spatial Domain
Spatial domain research focuses on understanding and leveraging spatial information within various data types, aiming to improve model performance and extract meaningful insights. Current research emphasizes the integration of spatial information with other modalities (temporal, semantic) using architectures like transformers, graph neural networks, and diffusion models, often incorporating attention mechanisms to enhance feature extraction and modeling of complex relationships. This work has significant implications across diverse fields, from improving image and video processing and analysis to enhancing autonomous navigation, medical image analysis, and urban planning through more accurate and efficient algorithms.
Papers
Discovering Dynamic Functional Brain Networks via Spatial and Channel-wise Attention
Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu, Shijie Zhao, Xintao Hu, Dajiang Zhu, Tianming Liu, Bao Ge
Bias Analysis of Spatial Coherence-Based RTF Vector Estimation for Acoustic Sensor Networks in a Diffuse Sound Field
Wiebke Middelberg, Simon Doclo