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MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition by Zhang, Fan; Guo, Xiaobao; Peng, Xiaojiang; Kot, Alex is a scholarly article available to read on EtoBox.

What is MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition about?

Cutting-edge research in facial expression recognition (FER) currently favors the utilization of convolutional neural networks (CNNs) backbone which is supervisedly pre-trained on face recognition datasets for feature extraction. However, due to the vast scale of face recognition datasets and the high cost associated with collecting facial labels, this pre-training paradigm incurs significant expenses. Towards this end, we propose to pre-train vision Transformers (ViTs) through a self-supervised approach on a mid-scale general image dataset. In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced. Therefore, we propose a contrastive fine-tuning approach to effectively mitigate this domain disparity. Specifically, we introduce a novel FER training paradigm named Mask Image pre-training with MIx Contrastive fine-tuning (MIMIC). In the initial phase, we pre-train the ViT via masked image reconstruction on general images. Subsequently, in the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive

Author

Zhang, Fan; Guo, Xiaobao; Peng, Xiaojiang; Kot, Alex

Published

2024

Language

EN