Topic modelling is the new revolution in text mining. It is a statistical technique for revealing
the underlying semantic structure in large collection of documents. After analysing …

As a paradigm to recover the sparse signal from a small set of linear measurements,
compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to …

Acoustic data provide scientific and engineering insights in fields ranging from biology and
communications to ocean and Earth science. We survey the recent advances and …

Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video
sequence. Existing mainstream VAD techniques are based on either the one-class …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

We present a novel approach to low-level vision problems that combines sparse coding and
deep networks pre-trained with denoising auto-encoder (DA). We propose an alternative …

Inspired by the great success of deep neural networks (DNNs), many unfolding methods
have been proposed to integrate traditional image modeling techniques, such as dictionary …

Person re-identification (Re-ID) poses a unique challenge to deep learning: how to learn a
deep model with millions of parameters on a small training set of few or no labels. In this …

Human face recognition have been an active research area for the last few decades.
Especially, during the last five years, it has gained significant research attention from …

Although disentangled representations are often said to be beneficial for downstream tasks,
current empirical and theoretical understanding is limited. In this work, we provide evidence …