Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Facial expression recognition (FER) is challenging due to the class imbalance caused by
data collection. Existing studies tackle the data bias problem using only labeled facial …

Recognizing multiple labels of an image is a practical yet challenging task, and remarkable
progress has been achieved by searching for semantic regions and exploiting label …

Facial expressions reflect people's feelings, emotions, and motives, attracting researchers to
develop a self-acting automatic facial expression recognition system. With the advances of …

Facial expression recognition (FER) has received significant attention in the past decade
with witnessed progress, but data inconsistencies among different FER datasets greatly …

As one of the most important affective signals, facial affect analysis (FAA) is essential for
developing human-computer interaction systems. Early methods focus on extracting …

Cross-domain Facial Expression Recognition (FER) aims to safely transfer the learned
knowledge from labeled source data to unlabeled target data, which is challenging due to …

Facial expression recognition (FER) in the wild is challenging due to various unconstrained
conditions, ie, occlusions and head pose variations. Previous methods tend to improve the …

Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer
their relationships for a given video. It requires not only a comprehensive understanding of …

Graph is powerful for representing various types of real-world data. The topology (edges'
presence) and edges' features of a graph decides the message passing mechanism among …