Multiview learning (MVL), which enhances the learners' performance by coordinating
complementarity and consistency among different views, has attracted much attention. The …

In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in
salient object detection. However, training such a deep model requires a large amount of …

Abstract Low Rank Regularization (LRR), in essence, involves introducing a low rank or
approximately low rank assumption to target we aim to learn, which has achieved great …

With the ability to exploit the internal structure of data, graph-based models have received a
lot of attention and have achieved great success in multiview subspace clustering for …

Domain adaptation aims to leverage knowledge from a well-labeled source domain to a
poorly labeled target domain. A majority of existing works transfer the knowledge at either …

Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which
is the most common scenario in real-world applications, is under insufficient exploration …

How do we retrieve images accurately? Also, how do we rank a group of images precisely
and efficiently for specific queries? These problems are critical for researchers and …

In real-world transfer learning tasks, especially in cross-modal applications, the source
domain and the target domain often have different features and distributions, which are well …

Zero-shot learning (ZSL) and cold-start recommendation (CSR) are two challenging
problems in computer vision and recommender system, respectively. In general, they are …

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic
properties of different views are diverse, even incompatible, few approaches implement …