  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.
