QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition
This addresses the problem of feature redundancy in facial expression recognition for computer vision applications, representing an incremental improvement with a novel network design.
The paper tackles the challenge of mixed labeled and unlabeled features in facial expression recognition by introducing Quadruplet Cross Similarity (QCS), which uses cross-attention and a four-branch network to refine features, achieving state-of-the-art performance on multiple datasets.
Facial expression recognition faces challenges where labeled significant features in datasets are mixed with unlabeled redundant ones. In this paper, we introduce Cross Similarity Attention (CSA) to mine richer intrinsic information from image pairs, overcoming a limitation when the Scaled Dot-Product Attention of ViT is directly applied to calculate the similarity between two different images. Based on CSA, we simultaneously minimize intra-class differences and maximize inter-class differences at the fine-grained feature level through interactions among multiple branches. Contrastive residual distillation is utilized to transfer the information learned in the cross module back to the base network. We ingeniously design a four-branch centrally symmetric network, named Quadruplet Cross Similarity (QCS), which alleviates gradient conflicts arising from the cross module and achieves balanced and stable training. It can adaptively extract discriminative features while isolating redundant ones. The cross-attention modules exist during training, and only one base branch is retained during inference, resulting in no increase in inference time. Extensive experiments show that our proposed method achieves state-of-the-art performance on several FER datasets.