The proliferation of edge devices has pushed computing from the cloud to the data sources,
and video analytics is among the most promising applications of edge computing. Running …

Collaborative inference enables mobile devices to offload the computation tasks and
support computation-intensive perception services such as object detection with lower …

Multi-view clustering aims to extract valuable information from different sources or
perspectives. Over the years, the deep neural network has demonstrated its superior …

In this paper, we investigate the research problem of unsupervised multi-view feature
selection. Conventional solutions first simply combine multiple pre-constructed view-specific …

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 …

Multi-view data often contain redundant information that cannot be simply spliced. Many
existing methods for processing them by assigning weights to each view cannot capture …

Deep multi-view representation learning focuses on training a unified low-dimensional
representation for data with multiple sources or modalities. With the rapidly growing attention …

Canonical correlation analysis (CCA) is an effective feature learning method, which has
wide applications in pattern recognition and computer vision. However, CCA considers the …

While self-supervised learning techniques are often used to mine hidden knowledge from
unlabeled data via modeling multiple views, it is unclear how to perform effective …

Recently, multi-view representation learning has gained rapid growth in various fields. Most
of previous multi-view learning methods rely on strong notions of distances that often …