Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling for ABAW Competition at CVPR2024
Addressing challenges in facial expression recognition through semi-supervised pretraining and temporal modeling for improved performance.
A3lign-DFER: Comprehensive Dynamic Affective Alignment for Facial Expression Recognition with CLIP
A3lign-DFER introduces a new paradigm for dynamic facial expression recognition, enhancing alignment and achieving state-of-the-art results.
Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition in ABAW Competition
MAE-FaceとFusion Attentionを統合した革新的なアプローチにより、ABAWコンペティションでの表情分類の性能向上を実証。
Multi-Modal Self-Supervised Learning for Facial Expression Recognition in the Wild
Employing multi-task multi-modal self-supervised learning to learn rich data representations for facial expression recognition from in-the-wild video data without requiring expensive annotations.
Adaptive Label Distribution Fusion Network for Robust Facial Expression Recognition
A dual-branch adaptive distribution fusion framework is proposed to address the ambiguity problem in facial expression recognition by mining class distributions of emotions and adaptively fusing them with label distributions of samples.
Facial Expression Recognition in Students Using an Extended Vision Long Short-Term Memory Network (xLSTM-FER)
xLSTM-FER, a novel architecture based on Extended Long Short-Term Memory (xLSTM), offers a computationally efficient and highly accurate method for recognizing student facial expressions, outperforming existing CNN and ViT-based approaches.
GReFEL: A Novel Approach to Facial Expression Learning Using Geometry-Aware Reliability Balancing for Improved Accuracy in Handling Bias and Imbalanced Datasets
GReFEL, a novel facial expression learning framework, leverages Vision Transformers and a geometry-aware reliability balancing module to improve accuracy and mitigate biases stemming from imbalanced datasets in facial expression recognition.
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