Abstract Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that
focuses on recognizing facial expressions in video format. Previous research has …

Graph neural networks have pushed state-of-the-arts in graph classifications recently.
Typically, these methods are studied within the context of supervised end-to-end training …

Facial expression data is characterized by a significant imbalance, with most collected data
showing happy or neutral expressions and fewer instances of fear or disgust. This …

Abstract Facial Expression Recognition (FER) plays a crucial role in computer vision and
finds extensive applications across various fields. This paper aims to present our approach …

In computer vision, emotion recognition using facial expression images is considered an
important research issue. Deep learning advances in recent years have aided in attaining …

Self-supervised learning for semi-supervised facial expression recognition aims to avoid the
need to collect expensive labeled facial expression data. Existing methods demonstrate an …

Abstract Facial Expression Recognition (FER) is an important task in computer vision and
has wide applications in many fields. In this paper, we introduce our approach to the fifth …

Most unconstrained facial expression recognition (FER) methods take original facial images
as inputs to learn discriminative features by well-designed loss functions, which cannot …

In this paper, we study the problem of semi-supervised 3D object detection, which is of great
importance considering the high annotation cost for cluttered 3D indoor scenes. We resort to …

Heterogeneous face recognition (HFR) aims to match input face identity across different
image modalities. Due to the existing large modality gap and the limited number of training …