# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 1 Introduction
To effectively enhance the ability of CNNs to capture sub-action information, two-stream methods [14] and temporal segment networks (TSNs) [15] utilize pre-extracted optical flow to extract fast motion-tempo information of sub-actions. However, time-consuming optical flow computation requires substantial computational resources. To this, Motion Squeeze [16] is proposed by employing lightweight motion-feature learning to replace the resource-intensive optical flow calculation, directly from adjacent video frames. Additionally, researchers have applied attention mechanisms to capture key sub-action information for video action recognition. For instance, attention modules [17; 18] that assess the importance of different parts of the input features are utilized along the temporal, channel, or spatial dimensions by calculating a set of weights. The weights are then applied to the input features to extract critical information within the corresponding dimensions. The self-attention-based modules are proposed [19; 20; 21; 22] for processing input features, by calculating the similarity between each position and all other positions of feature maps. Applying these weights to the input features allows us to capture global information more effectively. However, on one hand, the models used to capture the aforementioned sub-action information can effectively extract the sub-action's fast-tempo information and struggle to capture slow tempos. On the other hand, these attention modules operate in a feed-forward manner, where information flows from the input layer to the output layer in a single direction, relying solely on low-level features before the attention module generates attention weights. The feedforward manner has the advantages of computational simplicity and ease of training but limits the attention mechanism to the representational capacity and low-level semantic information of a single feature. Actually, in the visual system [23], high-level semantic information and task-specific contextual information can be obtained from high-level features through feedback connections, in which high-level information is transmitted back to lower layers. Feedback connections have been proven effective in the visual cortex of primates by neuroscientists [24] and are considered to be a key component of visual attention [25; 26]. Similarly, human visual perception transmits high-level semantic information through feedback connections and attention, selectively enhancing and suppressing neuronal activation [27]. Therefore, inspired and motivated by the aforementioned truths, we propose a Multi-Granularity Adaptive Fusion Module (MgAFM), to capture sub-action temporal information at various motion tempos and fuse it with the original RGB features for later representation learning. MgAFM captures crucial sub-action intrinsic information by adaptive motion dynamic features fusion at different granularities. On the other hand, we propose a Feedback Attention-Guided Module (FAGM), which integrates the action semantic and action category-specific contextual information in a feedback manner. FAGM aims to guide the low-level feature learning by the high-level contextual information, focusing on sub-action spatio-temporal and channel-wise information, enhancing the model to focus on information crucial for action classification. In summary, we propose the Sub-action Motion information Enhancement Network (SMEN), mainly consisting of MgAFM and FAGM, designed to improve action classification performance by effectively capturing and discriminating sub-actions with diverse motion tempos. The main contributions of this paper can be summarized as follows:  
* To capture sub-action motion tempos that characterize the dynamic motion information at different temporal granularities, we propose a Multi-Granularity Adaptive Fusion Module (MgAFM). This module captures sub-action temporal information at various motion tempos and adaptively fuses it with the original RGB features for subsequent representation learning.