Face super-resolution (FSR), also known as face hallucination, which is aimed at enhancing
the resolution of low-resolution (LR) face images to generate high-resolution face images, is …

Existing text-to-image synthesis methods generally are only applicable to words in the
training dataset. However, human faces are so variable to be described with limited words …

Subgraph recognition aims at discovering a compressed substructure of a graph that is most
informative to the graph property. It can be formulated by optimizing Graph Information …

Near-infrared to visible (NIR-VIS) face recognition is the most common case in
heterogeneous face recognition, which aims to match a pair of face images captured from …

Heterogeneous face recognition (HFR) aims at matching face images across different
domains. It is challenging due to the severe domain discrepancies and overfitting caused by …

The cross-sensor gap is one of the challenges that have aroused much research interests in
Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the …

The goal of molecule optimization is to optimize molecular properties by modifying molecule
structures. Conditional generative models provide a promising way to transfer the input …

Near-infrared and visible face recognition (NIR-VIS) is attracting increasing attention
because of the need to achieve face recognition in low-light conditions to enable 24-hour …

Near infrared (NIR) to Visible (VIS) face matching is challenging due to the significant
domain gaps as well as a lack of sufficient data for cross-modality model training. To …

As the most important topic in Heterogeneous Face Recognition (HFR), Near-InfraRed and
VISual (NIR-VIS) face recognition has attracted increasing research attentions owing to its …