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
MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction
Xiaohong Fan, Ke Chen, Huaming Yi, Yin Yang, Jianping Zhang
TAGA: Text-Attributed Graph Self-Supervised Learning by Synergizing Graph and Text Mutual Transformations
Zheng Zhang, Yuntong Hu, Bo Pan, Chen Ling, Liang Zhao
Classifying geospatial objects from multiview aerial imagery using semantic meshes
David Russell, Ben Weinstein, David Wettergreen, Derek Young
Lens functions for exploring UMAP Projections with Domain Knowledge
Daniel M. Bot, Jan Aerts
RobustMVS: Single Domain Generalized Deep Multi-view Stereo
Hongbin Xu, Weitao Chen, Baigui Sun, Xuansong Xie, Wenxiong Kang
Deep Models for Multi-View 3D Object Recognition: A Review
Mona Alzahrani, Muhammad Usman, Salma Kammoun, Saeed Anwar, Tarek Helmy
Multi-view Content-aware Indexing for Long Document Retrieval
Kuicai Dong, Derrick Goh Xin Deik, Yi Quan Lee, Hao Zhang, Xiangyang Li, Cong Zhang, Yong Liu
Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Hao Miao, Senzhang Wang, Meiyue Zhang, Diansheng Guo, Funing Sun, Fan Yang