FER-former: Multi-modal Transformer for Facial Expression Recognition

• URL: http://arxiv.org/abs/2303.12997v1
• Date: Thu, 23 Mar 2023 02:29:53 GMT
• Title: FER-former: Multi-modal Transformer for Facial Expression Recognition
• Authors: Yande Li, Mingjie Wang, Minglun Gong, Yonggang Lu, Li Liu
• Abstract summary: A novel multifarious supervision-steering Transformer for Facial Expression Recognition is proposed in this paper. Our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. Experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts.
• Score: 14.219492977523682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-increasing demands for intuitive interactions in Virtual Reality has triggered a boom in the realm of Facial Expression Recognition (FER). To address the limitations in existing approaches (e.g., narrow receptive fields and homogenous supervisory signals) and further cement the capacity of FER tools, a novel multifarious supervision-steering Transformer for FER in the wild is proposed in this paper. Referred as FER-former, our approach features multi-granularity embedding integration, hybrid self-attention scheme, and heterogeneous domain-steering supervision. In specific, to dig deep into the merits of the combination of features provided by prevailing CNNs and Transformers, a hybrid stem is designed to cascade two types of learning paradigms simultaneously. Wherein, a FER-specific transformer mechanism is devised to characterize conventional hard one-hot label-focusing and CLIP-based text-oriented tokens in parallel for final classification. To ease the issue of annotation ambiguity, a heterogeneous domains-steering supervision module is proposed to make image features also have text-space semantic correlations by supervising the similarity between image features and text features. On top of the collaboration of multifarious token heads, diverse global receptive fields with multi-modal semantic cues are captured, thereby delivering superb learning capability. Extensive experiments on popular benchmarks demonstrate the superiority of the proposed FER-former over the existing state-of-the-arts.

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