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
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling
Hao Wang, Zhi-Qi Cheng, Jingdong Sun, Xin Yang, Xiao Wu, Hongyang Chen, Yan Yang
Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild
Jiatai Wang, Zhiwei Xu, Xuewen Yang, Xin Wang