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
Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles
A. Quadir, Mushir Akhtar, M. Tanveer
MV-DETR: Multi-modality indoor object detection by Multi-View DEtecton TRansformers
Zichao Dong, Yilin Zhang, Xufeng Huang, Hang Ji, Zhan Shi, Xin Zhan, Junbo Chen