# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 5 Conclusions  
In this paper, to explore the potential of the human action-recognition model for capturing and representing vital sub-action information with different motion tempos, we propose a Sub-action Motion information Enhancement Network (SMEN) based on motion tempo learning and feedback attention, comprising two key components: the Multi-Granularity Adaptive Fusion Module (MgAFM) and the Feedback Attention-Guided Module (FAGM). The MgAFM captures the intrinsic information of sub-actions with motion tempos at different granularities and adaptively fuses it with the original RGB features. The FAGM leverages global contextual information to guide low-level feature learning with a feedback mechanism, focusing on vital spatio-temporal and channel-wise information. Extensive experiments on three public datasets, ablation studies, and visualization analyses demonstrate that the proposed method outperforms compared methods, especially for videos containing sub-actions with different motion tempos.  
The proposed SMEN focuses less on the contextual background information of human actions and the object categories involved in interactive actions, which is useful for complex human activities. The future plan is to optimize motion-extraction methods to better capture the contextual background of actions or explore the generalization of SMEN using heatmaps [47], which are converted from human skeleton data for skeleton-based human action recognition.  
Conceptualization, C.L. and Y.L.; Data curation, Y.L.; Formal analysis, Y.L.; Funding acquisition, C.L.; Investigation, Y.L., S.J. and P.Z.; Methodology, Y.L. and C.L.; Project administration, C.L.; Resources, C.L.; Software, Y.L. and S.J.; Supervision, C.L.; Validation, C.L. and Y.L.; Visualization, Y.L., S.J. and P.Z.; Writing--original draft preparation, Y.L.; Writing--review and editing, C.L., S.J. and P.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 11: On the Something-Something V2 dataset, we use Grad-CAM [46] to visualize the activation maps for the actions “Covering something with something” and “Holding something over something”, with deep features extracted using TSM and the proposed SME Nm ethod, respectively.  
**Funding:** This research was founded in part by the National Natural Science Foundation of China under Grant 62176086, and in part by the Science and Technology Development of Henan Province under Grant 242102211055 and 242102210032, the Soft Science Research Program of Henan Province under Grant 252400410618.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Something-Something V1 dataset can be downloaded from [[https://aistudio.baidu.com/datasetdetail/44373](https://aistudio.baidu.com/datasetdetail/44373)]([https://aistudio.baidu.com/datasetdetail/44373](https://aistudio.baidu.com/datasetdetail/44373)) (accessed on 20 February 2025). The Something-Something V2 dataset can be downloaded from [[https://www.qualcomm.com/developer/software/something-something-v-2-dataset](https://www.qualcomm.com/developer/software/something-something-v-2-dataset)]([https://www.qualcomm.com/developer/software/something-something-v-2-dataset](https://www.qualcomm.com/developer/software/something-something-v-2-dataset)) (accessed on 20 February 2025). The Kinetics-400 dataset can be downloaded from [[https://github.com/cvdfoundation/kinetics-dataset](https://github.com/cvdfoundation/kinetics-dataset)]([https://github.com/cvdfoundation/kinetics-dataset](https://github.com/cvdfoundation/kinetics-dataset)) (accessed on 20 February 2025).  
**Acknowledgments:** The authors sincerely thank [PERSON] for her constructive comments, which substantially improved the overall quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.