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
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection
Alvaro Fernandez-Quilez, Linas Vidziunas, Ørjan Kløvfjell Thoresen, Ketil Oppedal, Svein Reidar Kjosavik, Trygve Eftestøl
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Tim Nikolass Lindeijer, Tord Martin Ytredal, Trygve Eftestøl, Tobias Nordström, Fredrik Jäderling, Martin Eklund, Alvaro Fernandez-Quilez
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
Chunpeng Zhou, Kangjie Ning, Haishuai Wang, Zhi Yu, Sheng Zhou, Jiajun Bu
Multi-modal Multi-view Clustering based on Non-negative Matrix Factorization
Yasser Khalafaoui, Nistor Grozavu, Basarab Matei, Laurent-Walter Goix
Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion
C. I. Ugwu, S. Casarin, O. Lanz
Multi-View Vertebra Localization and Identification from CT Images
Han Wu, Jiadong Zhang, Yu Fang, Zhentao Liu, Nizhuan Wang, Zhiming Cui, Dinggang Shen
SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation
Yiqing Wang, Zihan Li, Jieru Mei, Zihao Wei, Li Liu, Chen Wang, Shengtian Sang, Alan Yuille, Cihang Xie, Yuyin Zhou