Dynamic facial expression recognition (DFER) in the wild is still hindered by data limitations,
eg, insufficient quantity and diversity of pose, occlusion and illumination, as well as the …

Music-driven choreography is a challenging problem with a wide variety of industrial
applications. Recently, many methods have been proposed to synthesize dance motions …

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 …

Feature embeddings derived from continuous mapping using the deep neural network are
critical for accurate classification in seizure prediction tasks. However, the embeddings of …

Facial expression recognition (FER) remains a challenging task due to the ambiguity of
expressions. The derived noisy labels significantly harm the performance in real-world …

To better characterize the differences in category features in Facial Expression Recognition
(FER) tasks, and improve inter-class separability and intra-class compactness, we propose a …

Representation learning and feature disentanglement have garnered significant research
interest in the field of facial expression recognition (FER). The inherent ambiguity of emotion …

Facial expression recognition (FER) tasks with convolutional neural networks (CNNs) have
seen remarkable progress. However, these CNN-based approaches do not well capture …

Audio-driven talking head animation is a challenging research topic with many real-world
applications. Recent works have focused on creating photo-realistic 2D animation while …

In this paper, we introduce a framework ARBEx, a novel attentive feature extraction
framework driven by Vision Transformer with reliability balancing to cope against poor class …