Multi-view inference can utilize visual information from several views like a human being
and significantly improve accuracy in some scenes, but it inevitably incurs more computing …

The classification performance of multi-view learning is better than the traditional machine
learning algorithms as stated in state-of-art. In this research, using the graph coloring …

Learning multi-view data is a central topic for advanced deep model applications. Existing
efforts mainly focus on exploring shared information to maximize the consensus among all …

Multi-view learning aims to explore a global common structure shared by different views
collected from multiple individual sources. The nascent field of federated learning tries to …

Multi-view object detection or the problem of detecting an object using multiple viewpoints, is
an important problem in computer vision with varied applications such as distributed smart …

In real scenarios, graph-based multiview clustering has clearly shown popularity owing to
the high efficiency in fusing the information from multiple views. Practically, the multiview …

We propose a communication-efficient collaborative inference framework in the domain of
edge inference, focusing on the efficient use of vision transformer (ViTs) models. The …

Real-world datasets often describe data instances in different views that complement
information for each other. Unfortunately, synthesizing these views for learning a …

Multiview learning has made significant progress in recent years. However, an implicit
assumption is that multiview data are complete, which is often contrary to practical …

Incomplete multi-view clustering becomes an important research problem, since multi-view
data with missing values are ubiquitous in real-world applications. Although great efforts …