Micro-actions are spontaneous body movements that indicate a person's true feelings and potential intentions, and micro-action recognition is important in human behavior analysis. Yet, recognizing micro-actions is challenging because they are subtle and appear for a very short time compared to normal actions. In this paper, we propose a micro-action recognition framework based on Hierarchical Fusion and Inference (HiFI) to capture subtle multimodal information. Specifically, we first hierarchically integrate multimodal local and global information, including the 2D key-points of faces, hands and bodies, the depth information, and the RGB image sequences. Afterward, both 3D-CNNs and Transformers are used to effectively capture local and long-range dependence. Finally, we propose a novel from-fine-to-coarse (F2C) inference strategy, based on hybrid ensemble of multi-branches, to boost the accuracy and credibility of coarse action recognition. Our solution ranked 4th in the MAC Challenge Track 1.