EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition

ArXi:2604.08106v1 Announce Type: new Micro-expression recognition can obtain the real emotion of the individual at the current moment. Although deep learning-based methods, especially Transformer-based methods, have achieved impressive results, these methods have high computational complexity due to the large number of tokens in the multi-head self-attention. In addition, the existing micro-expression datasets are small-scale, which makes it difficult for Transformer-based models to learn effective micro-expression representations.