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-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks
Orchid Chetia Phukan, Devyani Koshal, Swarup Ranjan Behera, Arun Balaji Buduru, Rajesh Sharma
MultiCamCows2024 -- A Multi-view Image Dataset for AI-driven Holstein-Friesian Cattle Re-Identification on a Working Farm
Phoenix Yu, Tilo Burghardt, Andrew W Dowsey, Neill W Campbell
QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
Samuel Adebayo, Seán McLoone, Joost C. Dessing
Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection
Wenhua Dong, Xiao-Jun Wu, Zhenhua Feng, Sara Atito, Muhammad Awais, Josef Kittler