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
Boundary-Guided Learning for Gene Expression Prediction in Spatial Transcriptomics
Mingcheng Qu, Yuncong Wu, Donglin Di, Anyang Su, Tonghua Su, Yang Song, Lei Fan
ZipAR: Accelerating Auto-regressive Image Generation through Spatial Locality
Yefei He, Feng Chen, Yuanyu He, Shaoxuan He, Hong Zhou, Kaipeng Zhang, Bohan Zhuang
Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities
Zheyuan Zhang, Fengyuan Hu, Jayjun Lee, Freda Shi, Parisa Kordjamshidi, Joyce Chai, Ziqiao Ma
ISImed: A Framework for Self-Supervised Learning using Intrinsic Spatial Information in Medical Images
Nabil Jabareen, Dongsheng Yuan, Sören Lukassen