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

• URL: http://arxiv.org/abs/2401.07245v1
• Date: Sun, 14 Jan 2024 10:30:32 GMT
• Title: MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
• Authors: Fan Zhang, Xiaobao Guo, Xiaojiang Peng, Alex Kot
• Abstract summary: 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. In the fine-tuning stage, we introduce a mix-supervised contrastive learning process, which enhances the model with a more extensive range of positive samples.
• Score: 11.820043444385432
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 range of positive samples by the mixing strategy. Through extensive experiments conducted on three benchmark datasets, we demonstrate that our MIMIC outperforms the previous training paradigm, showing its capability to learn better representations. Remarkably, the results indicate that the vanilla ViT can achieve impressive performance without the need for intricate, auxiliary-designed modules. Moreover, when scaling up the model size, MIMIC exhibits no performance saturation and is superior to the current state-of-the-art methods.

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