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
Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection
Shunbo Dong, Jun Xue, Cunhang Fan, Kang Zhu, Yujie Chen, Zhao Lv
MIMIC: Masked Image Modeling with Image Correspondences
Kalyani Marathe, Mahtab Bigverdi, Nishat Khan, Tuhin Kundu, Patrick Howe, Sharan Ranjit S, Anand Bhattad, Aniruddha Kembhavi, Linda G. Shapiro, Ranjay Krishna
Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis
Marta Milovanović, Enzo Tartaglione, Marco Cagnazzo, Félix Henry
Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments
Jonas Hein, Nicola Cavalcanti, Daniel Suter, Lukas Zingg, Fabio Carrillo, Lilian Calvet, Mazda Farshad, Marc Pollefeys, Nassir Navab, Philipp Fürnstahl
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question
Yifan Wei, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu