In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

With a great success of the generative model via deep neural networks, monocular depth
estimation has been actively studied by exploiting various encoder-decoder architectures …

Hyperspectral image (HSI) consists of hundreds of continuous narrowbands with high
spectral correlation, which would lead to the so-called Hughes phenomenon and the high …

Recently, collaborative representation classification (CRC) has attracted much attention for
hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved …

Remote sensing scene categorization (RSSC) is a long-standing, vital, and complex issue in
computer vision. It seeks to classify a scene into one of the predetermined scene groups by …

GCN is a widely-used representation learning method for capturing hidden features in graph
data. However, traditional GCNs suffer from the over-smoothing problem, hindering their …

Learning from a limited number of labeled samples (pixels) remains a key challenge in the
hyperspectral image (HSI) classification. To address this issue, we propose a deep metric …

Introducing a handful of pairwise constraints into fuzzy clustering models to revise
memberships has been proven beneficial to boosting clustering performance. However …

Various applications of human-computer interaction are based on the estimation of head
pose, which is challenging due to different facial appearance, inhomogeneous illumination …

In this work we consider a generalized bilevel optimization framework for solving inverse
problems. We introduce fractional Laplacian as a regularizer to improve the reconstruction …