Multi View
Multi-view analysis integrates data from multiple perspectives to improve accuracy and robustness in various applications, primarily aiming to overcome limitations of single-view approaches. Current research focuses on developing efficient algorithms and model architectures, such as transformers and graph neural networks, to handle high-dimensional data and address challenges like data incompleteness, view misalignment, and computational constraints. This field is significant for advancing computer vision, medical image analysis, robotics, and other domains by enabling more accurate and reliable inferences from complex, multi-faceted data.
Papers
Mixture of multilayer stochastic block models for multiview clustering
Kylliann De Santiago, Marie Szafranski, Christophe Ambroise
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
Yuxiang Wei, Yuqian Chen, Tengfei Xue, Leo Zekelman, Nikos Makris, Yogesh Rathi, Weidong Cai, Fan Zhang, Lauren J. O' Donnell
Towards Establishing Dense Correspondence on Multiview Coronary Angiography: From Point-to-Point to Curve-to-Curve Query Matching
Yifan Wu, Rohit Jena, Mehmet Gulsun, Vivek Singh, Puneet Sharma, James C. Gee
A low-rank non-convex norm method for multiview graph clustering
Alaeddine Zahir, Khalide Jbilou, Ahmed Ratnani