Deep neural networks have demonstrated advanced abilities on various visual classification
tasks, which heavily rely on the large-scale training samples with annotated ground-truth …

Few-shot learning describes the challenging problem of recognizing samples from unseen
classes given very few labeled examples. In many cases, few-shot learning is cast as …

We have witnessed rapid advances in both face presentation attack models and
presentation attack detection (PAD) in recent years. When compared with widely studied 2D …

Driver behavior recognition has attracted extensive attention recently. Numerous methods
have been developed on the basis of various deep neural networks. However, the existing …

Despite the great success of the existing work in fine-grained visual categorization (FGVC),
there are still several unsolved challenges, eg, poor interpretation and vagueness …

18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) reveals altered brain
metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer's disease …

Many learning tasks are modeled as optimization problems with nonlinear constraints, such
as principal component analysis and fitting a Gaussian mixture model. A popular way to …

Question model has been widely concerned as the cornerstone of constructing Visual
Question Answering (VQA) models. Existing question models attempt to exploit word context …

Automatic surveillance of early neoplasia in Barrett's esophagus (BE) is of great significance
for improving the survival rate of esophageal cancer. It remains, however, a challenging task …

Audio-visual event localization aims to localize an event that is both audible and visible in
the wild, which is a widespread audio-visual scene analysis task for unconstrained videos …