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
SPAD : Spatially Aware Multiview Diffusers
Yash Kant, Ziyi Wu, Michael Vasilkovsky, Guocheng Qian, Jian Ren, Riza Alp Guler, Bernard Ghanem, Sergey Tulyakov, Igor Gilitschenski, Aliaksandr Siarohin
Scalable Multi-view Clustering via Explicit Kernel Features Maps
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
ConKeD: Multiview contrastive descriptor learning for keypoint-based retinal image registration
David Rivas-Villar, Álvaro S. Hervella, José Rouco, Jorge Novo
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing
Jaeill Kim, Duhun Hwang, Eunjung Lee, Jangwon Suh, Jimyeong Kim, Wonjong Rhee